Improved code structure and layout

app.py

@@ -1,142 +1,308 @@
-import pandas as pd
 import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
+import pandas as pd
 import gradio as gr
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import LabelEncoder, StandardScaler
-from sklearn.linear_model import LogisticRegression
-from sklearn.tree import DecisionTreeClassifier, plot_tree
-from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
-from sklearn.svm import SVC
-from sklearn.neighbors import KNeighborsClassifier
-from xgboost import XGBClassifier
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
-
-# Load dataset
-mydata = pd.read_csv("eda_heart.csv")
-
-
-# Remove duplicates
-mydata = mydata.drop_duplicates()
+import pickle
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
 
+# Load model weights and metadata
+with open('model_weights.pkl', 'rb') as f:
+    model_weights = pickle.load(f)
 
-# Outlier removal using IQR
-variables = ["Age", "RestingBP", "Cholesterol", "MaxHR", "Oldpeak"]
-for col in variables:
-    q1 = mydata[col].quantile(0.25)
-    q3 = mydata[col].quantile(0.75)
-    iqr = q3 - q1
-    lower_bound = q1 - 1.5 * iqr
-    upper_bound = q3 + 1.5 * iqr
-    mydata = mydata[(mydata[col] >= lower_bound) & (mydata[col] <= upper_bound)]
+with open('scaler.pkl', 'rb') as f:
+    scaler = pickle.load(f)
 
+with open('feature_names.pkl', 'rb') as f:
+    feature_names = pickle.load(f)
 
-# Label Encoding
-label_encoders = {}
-for col in ["Sex", "ChestPainType", "RestingECG", "ExerciseAngina", "ST_Slope"]:
-    le = LabelEncoder()
-    mydata[col] = le.fit_transform(mydata[col])
-    label_encoders[col] = le
+with open('continuous_cols.pkl', 'rb') as f:
+    continuous_cols = pickle.load(f)
 
+with open('n_models.pkl', 'rb') as f:
+    n_models = pickle.load(f)
 
-# Splitting Data
-X = mydata.drop("HeartDisease", axis=1)
-y = mydata["HeartDisease"]
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=123)
+# Load individual models
+models = {}
+model_names = [
+    "logistic_regression",
+    "decision_tree", 
+    "gradient_boosting",
+    "knn",
+    "svm",
+    "random_forest",
+    "xgboost"
+]
 
-
-# Scaling continuous variables
-scaler = StandardScaler()
-continuous_cols = ["Age", "RestingBP", "Cholesterol", "MaxHR", "Oldpeak"]
-X_train[continuous_cols] = scaler.fit_transform(X_train[continuous_cols])
-X_test[continuous_cols] = scaler.transform(X_test[continuous_cols])
-
-
-# Model Training and Evaluation
-models = {
-    "Logistic Regression": LogisticRegression(),
-    "Decision Tree": DecisionTreeClassifier(),
-    "Gradient Boosting": GradientBoostingClassifier(),
-    "KNN": KNeighborsClassifier(n_neighbors=5),
-    "SVM": SVC(kernel='linear'),
-    "Random Forest": RandomForestClassifier(n_estimators=100),
-    "XGBoost": XGBClassifier(use_label_encoder=False, eval_metric='logloss')
+model_display_names = {
+    "logistic_regression": "Logistic Regression",
+    "decision_tree": "Decision Tree",
+    "gradient_boosting": "Gradient Boosting",
+    "knn": "KNN",
+    "svm": "SVM",
+    "random_forest": "Random Forest",
+    "xgboost": "XGBoost"
 }
 
-results = []
-for name, model in models.items():
-    model.fit(X_train, y_train)
-    y_pred = model.predict(X_test)
-    accuracy = accuracy_score(y_test, y_pred)
-    precision = precision_score(y_test, y_pred)
-    recall = recall_score(y_test, y_pred)
-    f1 = f1_score(y_test, y_pred)
-    results.append([name, accuracy, precision, recall, f1])
-
-# Convert results to DataFrame
-results_df = pd.DataFrame(results, columns=["Model", "Accuracy", "Precision", "Recall", "F1_Score"])
-
-# Calculate metrics
-metrics = results_df[["Model", "Accuracy", "F1_Score", "Precision"]].copy()
-metrics["Metric"] = (metrics["Accuracy"] * metrics["F1_Score"]) / metrics["Precision"]
-
-# Calculate average metric and ratios
-avg_metric = metrics["Metric"].mean()
-metrics["Ratio"] = metrics["Metric"] / avg_metric
-
-# Create a dictionary for ratios
-ratios = dict(zip(metrics["Model"], metrics["Ratio"]))
+for model_name in model_names:
+    with open(f'model_{model_name}.pkl', 'rb') as f:
+        models[model_display_names[model_name]] = pickle.load(f)
 
+print(f"Loaded {len(models)} models successfully!")
 
+def create_gauge_chart(probability):
+    """Create a gauge chart for risk probability"""
+    # Determine color based on risk level
+    if probability < 30:
+        color = "#10b981"  # Green
+        risk_level = "Low Risk"
+    elif probability < 50:
+        color = "#f59e0b"  # Orange
+        risk_level = "Moderate Risk"
+    elif probability < 70:
+        color = "#ef4444"  # Red
+        risk_level = "High Risk"
+    else:
+        color = "#dc2626"  # Dark Red
+        risk_level = "Very High Risk"
+    
+    fig = go.Figure(go.Indicator(
+        mode = "gauge+number+delta",
+        value = probability,
+        domain = {'x': [0, 1], 'y': [0, 1]},
+        title = {'text': f"<b>{risk_level}</b>", 'font': {'size': 24, 'color': color}},
+        number = {'suffix': "%", 'font': {'size': 48, 'color': color}},
+        gauge = {
+            'axis': {'range': [None, 100], 'tickwidth': 2, 'tickcolor': "gray"},
+            'bar': {'color': color, 'thickness': 0.75},
+            'bgcolor': "white",
+            'borderwidth': 2,
+            'bordercolor': "gray",
+            'steps': [
+                {'range': [0, 30], 'color': '#d1fae5'},
+                {'range': [30, 50], 'color': '#fef3c7'},
+                {'range': [50, 70], 'color': '#fee2e2'},
+                {'range': [70, 100], 'color': '#fecaca'}
+            ],
+            'threshold': {
+                'line': {'color': "black", 'width': 4},
+                'thickness': 0.75,
+                'value': 50
+            }
+        }
+    ))
+    
+    fig.update_layout(
+        height=300,
+        margin=dict(l=20, r=20, t=80, b=20),
+        paper_bgcolor="white",
+        font={'family': "Arial"}
+    )
+    
+    return fig
 
-trained_models = {}
-for name, model in models.items():
-    model.fit(X_train, y_train)
-    trained_models[name] = model
+def create_summary_text(probability, weighted_sum, threshold, prediction_result, model_predictions, model_weights):
+    """Create formatted HTML summary"""
+    if weighted_sum > threshold:
+        status_color = "#dc2626"
+        status_icon = "⚠️"
+        recommendation = """
+        <div style='background-color: #fee2e2; padding: 15px; border-radius: 8px; border-left: 4px solid #dc2626;'>
+            <h3 style='color: #991b1b; margin-top: 0;'>⚠️ Action Recommended</h3>
+            <ul style='color: #7f1d1d; margin-bottom: 0;'>
+                <li style='color: #000000;'>Consult a healthcare professional for thorough evaluation</li>
+                <li style='color: #000000;'>Consider scheduling a cardiac check-up</li>
+                <li style='color: #000000;'>Monitor your symptoms closely</li>
+                <li style='color: #000000;'>Maintain a heart-healthy lifestyle</li>
+            </ul>
+        </div>
+        """
+    else:
+        status_color = "#10b981"
+        status_icon = "✓"
+        recommendation = """
+        <div style='background-color: #d1fae5; padding: 15px; border-radius: 8px; border-left: 4px solid #10b981;'>
+            <h3 style='color: #065f46; margin-top: 0;'>✓ Lower Risk Detected</h3>
+            <ul style='color: #064e3b; margin-bottom: 0;'>
+                <li style='color: #000000;'>Continue maintaining a healthy lifestyle</li>
+                <li style='color: #000000;'>Regular exercise and balanced diet recommended</li>
+                <li style='color: #000000;'>Periodic health check-ups are still important</li>
+                <li style='color: #000000;'>Stay aware of any changes in symptoms</li>
+            </ul>
+        </div>
+        """
+    
+    # Create model predictions breakdown
+    predictions_breakdown = ""
+    disease_count = sum(1 for p in model_predictions.values() if p == 1)
+    safe_count = len(model_predictions) - disease_count
+    
+    html = f"""
+    <div style='font-family: Arial, sans-serif; padding: 20px;'>      
+        {recommendation}
+        
+        <div style='background-color: #f3f4f6; padding: 15px; border-radius: 8px; margin-top: 20px;'>
+            <h4 style='color: #374151; margin-top: 0;'>How This Works</h4>
+            <p style='color: #6b7280; font-size: 14px; line-height: 1.6; margin-bottom: 0;'>
+                This prediction uses an ensemble of 7 different machine learning models. Each model's prediction is weighted based 
+                on its performance metrics. The final decision is made when the weighted sum exceeds the threshold.
+            </p>
+        </div>
+        
+        <div style='background-color: #fffbeb; padding: 12px; border-radius: 8px; margin-top: 15px; border-left: 4px solid #f59e0b;'>
+            <p style='color: #92400e; font-size: 13px; margin: 0;'>
+                Medical Disclaimer: This tool is for educational purposes only and should not replace 
+                professional medical advice, diagnosis, or treatment. Always consult with qualified healthcare providers 
+                for medical decisions.
+            </p>
+        </div>
+    </div>
+    """
+    
+    return html
 
 def predict_heart_disease(age, resting_bp, cholesterol, max_hr, oldpeak, fasting_bs, sex, chest_pain_type, resting_ecg, exercise_angina, st_slope):
-    sex = 1 if sex == "Male" else 0  
-    chest_pain_type_mapping = {"ASY": 1, "ATA": 2, "NAP": 3, "TA": 4}
-    chest_pain_type = chest_pain_type_mapping[chest_pain_type]  
-    resting_ecg_mapping = {"Normal": 1, "ST": 2, "LVH": 3}
-    resting_ecg = resting_ecg_mapping[resting_ecg]  
+    # Encode categorical inputs using LabelEncoder compatible mappings
+    # These must match what LabelEncoder produced during training
+    sex_mapping = {"Male": 1, "Female": 0}
+    sex = sex_mapping.get(sex, 0)
+    
+    # Convert fasting_bs from string to integer
+    fasting_bs = int(fasting_bs)
+    
+    # For string categorical values, LabelEncoder sorts them alphabetically:
+    # ChestPainType: ["ASY", "ATA", "NAP", "TA"] -> 0, 1, 2, 3
+    chest_pain_type_mapping = {"ASY": 0, "ATA": 1, "NAP": 2, "TA": 3}
+    chest_pain_type = chest_pain_type_mapping.get(chest_pain_type, 0)
+    
+    # RestingECG: ["LVH", "Normal", "ST"] -> 0, 1, 2
+    resting_ecg_mapping = {"LVH": 0, "Normal": 1, "ST": 2}
+    resting_ecg = resting_ecg_mapping.get(resting_ecg, 1)
+    
+    # ExerciseAngina: ["N", "Y"] -> 0, 1
     exercise_angina_mapping = {"No": 0, "Yes": 1}
-    exercise_angina = exercise_angina_mapping[exercise_angina]  
-    st_slope_mapping = {"Up": 1, "Flat": 2, "Down": 3}
-    st_slope = st_slope_mapping[st_slope]  
-    input_data = np.array([[age, resting_bp, cholesterol, max_hr, oldpeak, sex, chest_pain_type, resting_ecg, exercise_angina, st_slope, fasting_bs]])
-    input_df = pd.DataFrame(input_data, columns=["Age", "RestingBP", "Cholesterol", "MaxHR", "Oldpeak", "Sex", "ChestPainType", "RestingECG", "ExerciseAngina", "ST_Slope", "FastingBS"])
-    input_df = input_df[X_train.columns] 
+    exercise_angina = exercise_angina_mapping.get(exercise_angina, 0)
+    
+    # ST_Slope: ["Down", "Flat", "Up"] -> 0, 1, 2
+    st_slope_mapping = {"Down": 0, "Flat": 1, "Up": 2}
+    st_slope = st_slope_mapping.get(st_slope, 2)
+    
+    # Create input dataframe matching exact training column order
+    input_df = pd.DataFrame({
+        "Age": [age],
+        "Sex": [sex],
+        "ChestPainType": [chest_pain_type],
+        "RestingBP": [resting_bp],
+        "Cholesterol": [cholesterol],
+        "FastingBS": [fasting_bs],
+        "RestingECG": [resting_ecg],
+        "MaxHR": [max_hr],
+        "ExerciseAngina": [exercise_angina],
+        "Oldpeak": [oldpeak],
+        "ST_Slope": [st_slope]
+    })
+    
+    # Ensure columns match feature_names order exactly
+    input_df = input_df[feature_names]
+    
+    # Scale continuous features
     input_df[continuous_cols] = scaler.transform(input_df[continuous_cols])
-    predictions = {name: model.predict(input_df)[0] for name, model in trained_models.items()}
-    # Calculate weight values and sum them
-    total_weight = sum(prediction * ratios[name] for name, prediction in predictions.items())
-    # Determine output based on total weight
-    if total_weight > 3.5:
-        output = "You might get a heart stroke take precautions"
+    
+    # Get predictions from all models
+    model_predictions = {}
+    weighted_sum = 0
+    total_weights = sum(model_weights.values())
+    
+    for name, model in models.items():
+        prediction = model.predict(input_df)[0]
+        weight = model_weights[name]
+        model_predictions[name] = prediction
+        weighted_sum += prediction * weight
+    
+    # Calculate probability and threshold
+    threshold = n_models / 2
+    # Probability is the weighted sum normalized by total possible weighted sum
+    max_possible_sum = sum(model_weights.values())
+    probability = (weighted_sum / max_possible_sum) * 100
+    
+    # Determine final prediction
+    if weighted_sum > threshold:
+        result = "High Risk Detected"
     else:
-        output = "You are safe"
-    return output
+        result = "Low Risk - You Are Safe"
+    
+    # Create visualizations
+    gauge_chart = create_gauge_chart(probability)
+    summary_html = create_summary_text(probability, weighted_sum, threshold, result, model_predictions, model_weights)
+    return summary_html, gauge_chart
+
+# Create Gradio interface with custom CSS
+css = """
+.gradio-container {
+    font-family: 'Arial', sans-serif;
+}
+.output-html {
+    border: none !important;
+}
+"""
 
+with gr.Blocks(css=css, theme=gr.themes.Soft()) as iface:
+    gr.Markdown(
+        """
+        # HeartSense - Heart Disease Prediction System
+        ### Advanced ML Ensemble for Cardiovascular Risk Assessment
+        Enter patient details below to get a comprehensive risk analysis using 7 different machine learning models.
+        """
+    )
+    
+    with gr.Column():
+        with gr.Row(scale=1):
+            with gr.Column(scale=1):
+                gr.Markdown("### Patient Information")
+                age = gr.Number(label="Age", value=50)
+                sex = gr.Radio(["Female", "Male"], label="Sex", value="Male")
+                fasting_bs = gr.Radio(["0", "1"], label="Fasting Blood Sugar", value="0")
+            
+            with gr.Column(scale=1):
+                gr.Markdown("### Cardiac Measurements")
+                resting_bp = gr.Number(label="Resting BP (mm Hg)", value=120)
+                cholesterol = gr.Number(label="Cholesterol (mg/dL)", value=200)
+                max_hr = gr.Number(label="Max Heart Rate", value=150)
+                oldpeak = gr.Number(label="Oldpeak", value=0)
+            
+            with gr.Column(scale=1):
+                gr.Markdown("### Clinical Indicators")
+                chest_pain_type = gr.Radio(["ASY", "ATA", "NAP", "TA"], label="Chest Pain Type", value="ASY")
+                resting_ecg = gr.Radio(["Normal", "ST", "LVH"], label="Resting ECG", value="Normal")
+                exercise_angina = gr.Radio(["No", "Yes"], label="Exercise Angina", value="No")
+                st_slope = gr.Radio(["Up", "Flat", "Down"], label="ST Slope", value="Flat")
+            
+        predict_btn = gr.Button("Analyze Risk", variant="primary", size="lg")
+        
+        with gr.Column(scale=2):
+            gauge_output = gr.Plot(label="Risk Gauge")
+            summary_output = gr.HTML(label="Summary")
+    
+    gr.Markdown(
+        """
+        ### Example Cases
+        Try these example inputs to see how the system works:
+        """
+    )
+    
+    gr.Examples(
+        examples=[
+            [55, 130, 250, 140, 1.0, "0", "Male", "ASY", "Normal", "No", "Flat"],
+            [45, 110, 180, 160, 0, "0", "Female", "NAP", "Normal", "No", "Up"],
+            [60, 140, 280, 130, 2.0, "1", "Male", "ASY", "ST", "Yes", "Down"],
+        ],
+        inputs=[age, resting_bp, cholesterol, max_hr, oldpeak, fasting_bs, sex, chest_pain_type, resting_ecg, exercise_angina, st_slope],
+    )
+    
+    predict_btn.click(
+        fn=predict_heart_disease,
+        inputs=[age, resting_bp, cholesterol, max_hr, oldpeak, fasting_bs, sex, chest_pain_type, resting_ecg, exercise_angina, st_slope],
+        outputs=[summary_output, gauge_output]
+    )
 
-iface = gr.Interface(
-    fn=predict_heart_disease,
-    inputs=[
-        gr.Number(label="Age"),
-        gr.Number(label="RestingBP"),
-        gr.Number(label="Cholesterol"),
-        gr.Number(label="MaxHR"),
-        gr.Number(label="Oldpeak (0-2)"),
-        gr.Number(label="FastingBS"),
-        gr.Radio(["Female", "Male"], label="Sex"),
-        gr.Radio(["ASY", "ATA", "NAP", "TA"], label="Chest Pain Type"),
-        gr.Radio(["Normal", "ST", "LVH"], label="Resting ECG"),
-        gr.Radio(["No", "Yes"], label="Exercise Angina"),
-        gr.Radio(["Up", "Flat", "Down"], label="ST Slope"),
-    ],
-    outputs=gr.Text(),
-    title="Heart Disease Prediction",
-    description="Enter patient details to predict heart disease risk using multiple models."
-)
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()

continuous_cols.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c2ea12475e0930f836685e606159ea3e731b7a66e6d58a3bc1a9037292cd6da2
+size 66

eda_heart.csv

@@ -1,919 +0,0 @@
-Age,Sex,ChestPainType,RestingBP,Cholesterol,FastingBS,RestingECG,MaxHR,ExerciseAngina,Oldpeak,ST_Slope,HeartDisease
-40,M,ATA,140,289,0,Normal,172,N,0,Up,0
-49,F,NAP,160,180,0,Normal,156,N,1,Flat,1
-37,M,ATA,130,283,0,ST,98,N,0,Up,0
-48,F,ASY,138,214,0,Normal,108,Y,1.5,Flat,1
-54,M,NAP,150,195,0,Normal,122,N,0,Up,0
-39,M,NAP,120,339,0,Normal,170,N,0,Up,0
-45,F,ATA,130,237,0,Normal,170,N,0,Up,0
-54,M,ATA,110,208,0,Normal,142,N,0,Up,0
-37,M,ASY,140,207,0,Normal,130,Y,1.5,Flat,1
-48,F,ATA,120,284,0,Normal,120,N,0,Up,0
-37,F,NAP,130,211,0,Normal,142,N,0,Up,0
-58,M,ATA,136,164,0,ST,99,Y,2,Flat,1
-39,M,ATA,120,204,0,Normal,145,N,0,Up,0
-49,M,ASY,140,234,0,Normal,140,Y,1,Flat,1
-42,F,NAP,115,211,0,ST,137,N,0,Up,0
-54,F,ATA,120,273,0,Normal,150,N,1.5,Flat,0
-38,M,ASY,110,196,0,Normal,166,N,0,Flat,1
-43,F,ATA,120,201,0,Normal,165,N,0,Up,0
-60,M,ASY,100,248,0,Normal,125,N,1,Flat,1
-36,M,ATA,120,267,0,Normal,160,N,3,Flat,1
-43,F,TA,100,223,0,Normal,142,N,0,Up,0
-44,M,ATA,120,184,0,Normal,142,N,1,Flat,0
-49,F,ATA,124,201,0,Normal,164,N,0,Up,0
-44,M,ATA,150,288,0,Normal,150,Y,3,Flat,1
-40,M,NAP,130,215,0,Normal,138,N,0,Up,0
-36,M,NAP,130,209,0,Normal,178,N,0,Up,0
-53,M,ASY,124,260,0,ST,112,Y,3,Flat,0
-52,M,ATA,120,284,0,Normal,118,N,0,Up,0
-53,F,ATA,113,468,0,Normal,127,N,0,Up,0
-51,M,ATA,125,188,0,Normal,145,N,0,Up,0
-53,M,NAP,145,518,0,Normal,130,N,0,Flat,1
-56,M,NAP,130,167,0,Normal,114,N,0,Up,0
-54,M,ASY,125,224,0,Normal,122,N,2,Flat,1
-41,M,ASY,130,172,0,ST,130,N,2,Flat,1
-43,F,ATA,150,186,0,Normal,154,N,0,Up,0
-32,M,ATA,125,254,0,Normal,155,N,0,Up,0
-65,M,ASY,140,306,1,Normal,87,Y,1.5,Flat,1
-41,F,ATA,110,250,0,ST,142,N,0,Up,0
-48,F,ATA,120,177,1,ST,148,N,0,Up,0
-48,F,ASY,150,227,0,Normal,130,Y,1,Flat,0
-54,F,ATA,150,230,0,Normal,130,N,0,Up,0
-54,F,NAP,130,294,0,ST,100,Y,0,Flat,1
-35,M,ATA,150,264,0,Normal,168,N,0,Up,0
-52,M,NAP,140,259,0,ST,170,N,0,Up,0
-43,M,ASY,120,175,0,Normal,120,Y,1,Flat,1
-59,M,NAP,130,318,0,Normal,120,Y,1,Flat,0
-37,M,ASY,120,223,0,Normal,168,N,0,Up,0
-50,M,ATA,140,216,0,Normal,170,N,0,Up,0
-36,M,NAP,112,340,0,Normal,184,N,1,Flat,0
-41,M,ASY,110,289,0,Normal,170,N,0,Flat,1
-50,M,ASY,130,233,0,Normal,121,Y,2,Flat,1
-47,F,ASY,120,205,0,Normal,98,Y,2,Flat,1
-45,M,ATA,140,224,1,Normal,122,N,0,Up,0
-41,F,ATA,130,245,0,Normal,150,N,0,Up,0
-52,F,ASY,130,180,0,Normal,140,Y,1.5,Flat,0
-51,F,ATA,160,194,0,Normal,170,N,0,Up,0
-31,M,ASY,120,270,0,Normal,153,Y,1.5,Flat,1
-58,M,NAP,130,213,0,ST,140,N,0,Flat,1
-54,M,ASY,150,365,0,ST,134,N,1,Up,0
-52,M,ASY,112,342,0,ST,96,Y,1,Flat,1
-49,M,ATA,100,253,0,Normal,174,N,0,Up,0
-43,F,NAP,150,254,0,Normal,175,N,0,Up,0
-45,M,ASY,140,224,0,Normal,144,N,0,Up,0
-46,M,ASY,120,277,0,Normal,125,Y,1,Flat,1
-50,F,ATA,110,202,0,Normal,145,N,0,Up,0
-37,F,ATA,120,260,0,Normal,130,N,0,Up,0
-45,F,ASY,132,297,0,Normal,144,N,0,Up,0
-32,M,ATA,110,225,0,Normal,184,N,0,Up,0
-52,M,ASY,160,246,0,ST,82,Y,4,Flat,1
-44,M,ASY,150,412,0,Normal,170,N,0,Up,0
-57,M,ATA,140,265,0,ST,145,Y,1,Flat,1
-44,M,ATA,130,215,0,Normal,135,N,0,Up,0
-52,M,ASY,120,182,0,Normal,150,N,0,Flat,1
-44,F,ASY,120,218,0,ST,115,N,0,Up,0
-55,M,ASY,140,268,0,Normal,128,Y,1.5,Flat,1
-46,M,NAP,150,163,0,Normal,116,N,0,Up,0
-32,M,ASY,118,529,0,Normal,130,N,0,Flat,1
-35,F,ASY,140,167,0,Normal,150,N,0,Up,0
-52,M,ATA,140,100,0,Normal,138,Y,0,Up,0
-49,M,ASY,130,206,0,Normal,170,N,0,Flat,1
-55,M,NAP,110,277,0,Normal,160,N,0,Up,0
-54,M,ATA,120,238,0,Normal,154,N,0,Up,0
-63,M,ASY,150,223,0,Normal,115,N,0,Flat,1
-52,M,ATA,160,196,0,Normal,165,N,0,Up,0
-56,M,ASY,150,213,1,Normal,125,Y,1,Flat,1
-66,M,ASY,140,139,0,Normal,94,Y,1,Flat,1
-65,M,ASY,170,263,1,Normal,112,Y,2,Flat,1
-53,F,ATA,140,216,0,Normal,142,Y,2,Flat,0
-43,M,TA,120,291,0,ST,155,N,0,Flat,1
-55,M,ASY,140,229,0,Normal,110,Y,0.5,Flat,0
-49,F,ATA,110,208,0,Normal,160,N,0,Up,0
-39,M,ASY,130,307,0,Normal,140,N,0,Up,0
-52,F,ATA,120,210,0,Normal,148,N,0,Up,0
-48,M,ASY,160,329,0,Normal,92,Y,1.5,Flat,1
-39,F,NAP,110,182,0,ST,180,N,0,Up,0
-58,M,ASY,130,263,0,Normal,140,Y,2,Flat,1
-43,M,ATA,142,207,0,Normal,138,N,0,Up,0
-39,M,NAP,160,147,1,Normal,160,N,0,Up,0
-56,M,ASY,120,85,0,Normal,140,N,0,Up,0
-41,M,ATA,125,269,0,Normal,144,N,0,Up,0
-65,M,ASY,130,275,0,ST,115,Y,1,Flat,1
-51,M,ASY,130,179,0,Normal,100,N,0,Up,0
-40,F,ASY,150,392,0,Normal,130,N,2,Flat,1
-40,M,ASY,120,466,1,Normal,152,Y,1,Flat,1
-46,M,ASY,118,186,0,Normal,124,N,0,Flat,1
-57,M,ATA,140,260,1,Normal,140,N,0,Up,0
-48,F,ASY,120,254,0,ST,110,N,0,Up,0
-34,M,ATA,150,214,0,ST,168,N,0,Up,0
-50,M,ASY,140,129,0,Normal,135,N,0,Up,0
-39,M,ATA,190,241,0,Normal,106,N,0,Up,0
-59,F,ATA,130,188,0,Normal,124,N,1,Flat,0
-57,M,ASY,150,255,0,Normal,92,Y,3,Flat,1
-47,M,ASY,140,276,1,Normal,125,Y,0,Up,0
-38,M,ATA,140,297,0,Normal,150,N,0,Up,0
-49,F,NAP,130,207,0,ST,135,N,0,Up,0
-33,F,ASY,100,246,0,Normal,150,Y,1,Flat,1
-38,M,ASY,120,282,0,Normal,170,N,0,Flat,1
-59,F,ASY,130,338,1,ST,130,Y,1.5,Flat,1
-35,F,TA,120,160,0,ST,185,N,0,Up,0
-34,M,TA,140,156,0,Normal,180,N,0,Flat,1
-47,F,NAP,135,248,1,Normal,170,N,0,Flat,1
-52,F,NAP,125,272,0,Normal,139,N,0,Up,0
-46,M,ASY,110,240,0,ST,140,N,0,Up,0
-58,F,ATA,180,393,0,Normal,110,Y,1,Flat,1
-58,M,ATA,130,230,0,Normal,150,N,0,Up,0
-54,M,ATA,120,246,0,Normal,110,N,0,Up,0
-34,F,ATA,130,161,0,Normal,190,N,0,Up,0
-48,F,ASY,108,163,0,Normal,175,N,2,Up,0
-54,F,ATA,120,230,1,Normal,140,N,0,Up,0
-42,M,NAP,120,228,0,Normal,152,Y,1.5,Flat,0
-38,M,NAP,145,292,0,Normal,130,N,0,Up,0
-46,M,ASY,110,202,0,Normal,150,Y,0,Flat,1
-56,M,ASY,170,388,0,ST,122,Y,2,Flat,1
-56,M,ASY,150,230,0,ST,124,Y,1.5,Flat,1
-61,F,ASY,130,294,0,ST,120,Y,1,Flat,0
-49,M,NAP,115,265,0,Normal,175,N,0,Flat,1
-43,F,ATA,120,215,0,ST,175,N,0,Up,0
-39,M,ATA,120,241,0,ST,146,N,2,Up,0
-54,M,ASY,140,166,0,Normal,118,Y,0,Flat,1
-43,M,ASY,150,247,0,Normal,130,Y,2,Flat,1
-52,M,ASY,160,331,0,Normal,94,Y,2.5,Flat,1
-50,M,ASY,140,341,0,ST,125,Y,2.5,Flat,1
-47,M,ASY,160,291,0,ST,158,Y,3,Flat,1
-53,M,ASY,140,243,0,Normal,155,N,0,Up,0
-56,F,ATA,120,279,0,Normal,150,N,1,Flat,1
-39,M,ASY,110,273,0,Normal,132,N,0,Up,0
-42,M,ATA,120,198,0,Normal,155,N,0,Up,0
-43,F,ATA,120,249,0,ST,176,N,0,Up,0
-50,M,ATA,120,168,0,Normal,160,N,0,Up,0
-54,M,ASY,130,603,1,Normal,125,Y,1,Flat,1
-39,M,ATA,130,215,0,Normal,120,N,0,Up,0
-48,M,ATA,100,159,0,Normal,100,N,0,Up,0
-40,M,ATA,130,275,0,Normal,150,N,0,Up,0
-55,M,ASY,120,270,0,Normal,140,N,0,Up,0
-41,M,ATA,120,291,0,ST,160,N,0,Up,0
-56,M,ASY,155,342,1,Normal,150,Y,3,Flat,1
-38,M,ASY,110,190,0,Normal,150,Y,1,Flat,1
-49,M,ASY,140,185,0,Normal,130,N,0,Up,0
-44,M,ASY,130,290,0,Normal,100,Y,2,Flat,1
-54,M,ATA,160,195,0,ST,130,N,1,Up,0
-59,M,ASY,140,264,1,LVH,119,Y,0,Flat,1
-49,M,ASY,128,212,0,Normal,96,Y,0,Flat,1
-47,M,ATA,160,263,0,Normal,174,N,0,Up,0
-42,M,ATA,120,196,0,Normal,150,N,0,Up,0
-52,F,ATA,140,225,0,Normal,140,N,0,Up,0
-46,M,TA,140,272,1,Normal,175,N,2,Flat,1
-50,M,ASY,140,231,0,ST,140,Y,5,Flat,1
-48,M,ATA,140,238,0,Normal,118,N,0,Up,0
-58,M,ASY,135,222,0,Normal,100,N,0,Up,0
-58,M,NAP,140,179,0,Normal,160,N,0,Up,0
-29,M,ATA,120,243,0,Normal,160,N,0,Up,0
-40,M,NAP,140,235,0,Normal,188,N,0,Up,0
-53,M,ATA,140,320,0,Normal,162,N,0,Up,0
-49,M,NAP,140,187,0,Normal,172,N,0,Up,0
-52,M,ASY,140,266,0,Normal,134,Y,2,Flat,1
-43,M,ASY,140,288,0,Normal,135,Y,2,Flat,1
-54,M,ASY,140,216,0,Normal,105,N,1.5,Flat,1
-59,M,ATA,140,287,0,Normal,150,N,0,Up,0
-37,M,NAP,130,194,0,Normal,150,N,0,Up,0
-46,F,ASY,130,238,0,Normal,90,N,0,Up,0
-52,M,ASY,130,225,0,Normal,120,Y,2,Flat,1
-51,M,ATA,130,224,0,Normal,150,N,0,Up,0
-52,M,ASY,140,404,0,Normal,124,Y,2,Flat,1
-46,M,ASY,110,238,0,ST,140,Y,1,Flat,0
-54,F,ATA,160,312,0,Normal,130,N,0,Up,0
-58,M,NAP,160,211,1,ST,92,N,0,Flat,1
-58,M,ATA,130,251,0,Normal,110,N,0,Up,0
-41,M,ASY,120,237,1,Normal,138,Y,1,Flat,1
-50,F,ASY,120,328,0,Normal,110,Y,1,Flat,0
-53,M,ASY,180,285,0,ST,120,Y,1.5,Flat,1
-46,M,ASY,180,280,0,ST,120,N,0,Up,0
-50,M,ATA,170,209,0,ST,116,N,0,Up,0
-48,M,ATA,130,245,0,Normal,160,N,0,Up,0
-45,M,NAP,135,192,0,Normal,110,N,0,Up,0
-41,F,ATA,125,184,0,Normal,180,N,0,Up,0
-62,F,TA,160,193,0,Normal,116,N,0,Up,0
-49,M,ASY,120,297,0,Normal,132,N,1,Flat,0
-42,M,ATA,150,268,0,Normal,136,N,0,Up,0
-53,M,ASY,120,246,0,Normal,116,Y,0,Flat,1
-57,F,TA,130,308,0,Normal,98,N,1,Flat,0
-47,M,TA,110,249,0,Normal,150,N,0,Up,0
-46,M,NAP,120,230,0,Normal,150,N,0,Up,0
-42,M,NAP,160,147,0,Normal,146,N,0,Up,0
-31,F,ATA,100,219,0,ST,150,N,0,Up,0
-56,M,ATA,130,184,0,Normal,100,N,0,Up,0
-50,M,ASY,150,215,0,Normal,140,Y,0,Up,0
-35,M,ATA,120,308,0,LVH,180,N,0,Up,0
-35,M,ATA,110,257,0,Normal,140,N,0,Flat,1
-28,M,ATA,130,132,0,LVH,185,N,0,Up,0
-54,M,ASY,125,216,0,Normal,140,N,0,Flat,1
-48,M,ASY,106,263,1,Normal,110,N,0,Flat,1
-50,F,NAP,140,288,0,Normal,140,Y,0,Flat,1
-56,M,NAP,130,276,0,Normal,128,Y,1,Up,0
-56,F,NAP,130,219,0,ST,164,N,0,Up,0
-47,M,ASY,150,226,0,Normal,98,Y,1.5,Flat,1
-30,F,TA,170,237,0,ST,170,N,0,Up,0
-39,M,ASY,110,280,0,Normal,150,N,0,Flat,1
-54,M,NAP,120,217,0,Normal,137,N,0,Up,0
-55,M,ATA,140,196,0,Normal,150,N,0,Up,0
-29,M,ATA,140,263,0,Normal,170,N,0,Up,0
-46,M,ASY,130,222,0,Normal,112,N,0,Flat,1
-51,F,ASY,160,303,0,Normal,150,Y,1,Flat,1
-48,F,NAP,120,195,0,Normal,125,N,0,Up,0
-33,M,NAP,120,298,0,Normal,185,N,0,Up,0
-55,M,ATA,120,256,1,Normal,137,N,0,Up,0
-50,M,ASY,145,264,0,Normal,150,N,0,Flat,1
-53,M,NAP,120,195,0,Normal,140,N,0,Up,0
-38,M,ASY,92,117,0,Normal,134,Y,2.5,Flat,1
-41,M,ATA,120,295,0,Normal,170,N,0,Up,0
-37,F,ASY,130,173,0,ST,184,N,0,Up,0
-37,M,ASY,130,315,0,Normal,158,N,0,Up,0
-40,M,NAP,130,281,0,Normal,167,N,0,Up,0
-38,F,ATA,120,275,0,Normal,129,N,0,Up,0
-41,M,ASY,112,250,0,Normal,142,N,0,Up,0
-54,F,ATA,140,309,0,ST,140,N,0,Up,0
-39,M,ATA,120,200,0,Normal,160,Y,1,Flat,0
-41,M,ASY,120,336,0,Normal,118,Y,3,Flat,1
-55,M,TA,140,295,0,Normal,136,N,0,Flat,1
-48,M,ASY,160,355,0,Normal,99,Y,2,Flat,1
-48,M,ASY,160,193,0,Normal,102,Y,3,Flat,1
-55,M,ATA,145,326,0,Normal,155,N,0,Up,0
-54,M,ASY,200,198,0,Normal,142,Y,2,Flat,1
-55,M,ATA,160,292,1,Normal,143,Y,2,Flat,1
-43,F,ATA,120,266,0,Normal,118,N,0,Up,0
-48,M,ASY,160,268,0,Normal,103,Y,1,Flat,1
-54,M,TA,120,171,0,Normal,137,N,2,Up,0
-54,M,NAP,120,237,0,Normal,150,Y,1.5,Flat,1
-48,M,ASY,122,275,1,ST,150,Y,2,Down,1
-45,M,ASY,130,219,0,ST,130,Y,1,Flat,1
-49,M,ASY,130,341,0,Normal,120,Y,1,Flat,1
-44,M,ASY,135,491,0,Normal,135,N,0,Flat,1
-48,M,ASY,120,260,0,Normal,115,N,2,Flat,1
-61,M,ASY,125,292,0,ST,115,Y,0,Up,0
-62,M,ATA,140,271,0,Normal,152,N,1,Up,0
-55,M,ASY,145,248,0,Normal,96,Y,2,Flat,1
-53,F,NAP,120,274,0,Normal,130,N,0,Up,0
-55,F,ATA,130,394,0,LVH,150,N,0,Up,0
-36,M,NAP,150,160,0,Normal,172,N,0,Up,0
-51,F,NAP,150,200,0,Normal,120,N,0.5,Up,0
-55,F,ATA,122,320,0,Normal,155,N,0,Up,0
-46,M,ATA,140,275,0,Normal,165,Y,0,Up,0
-54,F,ATA,120,221,0,Normal,138,N,1,Up,0
-46,M,ASY,120,231,0,Normal,115,Y,0,Flat,1
-59,M,ASY,130,126,0,Normal,125,N,0,Flat,1
-47,M,NAP,140,193,0,Normal,145,Y,1,Flat,1
-54,M,ATA,160,305,0,Normal,175,N,0,Up,0
-52,M,ASY,130,298,0,Normal,110,Y,1,Flat,1
-34,M,ATA,98,220,0,Normal,150,N,0,Up,0
-54,M,ASY,130,242,0,Normal,91,Y,1,Flat,1
-47,F,NAP,130,235,0,Normal,145,N,2,Flat,0
-45,M,ASY,120,225,0,Normal,140,N,0,Up,0
-32,F,ATA,105,198,0,Normal,165,N,0,Up,0
-55,M,ASY,140,201,0,Normal,130,Y,3,Flat,1
-55,M,NAP,120,220,0,LVH,134,N,0,Up,0
-45,F,ATA,180,295,0,Normal,180,N,0,Up,0
-59,M,NAP,180,213,0,Normal,100,N,0,Up,0
-51,M,NAP,135,160,0,Normal,150,N,2,Flat,1
-52,M,ASY,170,223,0,Normal,126,Y,1.5,Flat,1
-57,F,ASY,180,347,0,ST,126,Y,0.8,Flat,0
-54,F,ATA,130,253,0,ST,155,N,0,Up,0
-60,M,NAP,120,246,0,LVH,135,N,0,Up,0
-49,M,ASY,150,222,0,Normal,122,N,2,Flat,1
-51,F,NAP,130,220,0,Normal,160,Y,2,Up,0
-55,F,ATA,110,344,0,ST,160,N,0,Up,0
-42,M,ASY,140,358,0,Normal,170,N,0,Up,0
-51,F,NAP,110,190,0,Normal,120,N,0,Up,0
-59,M,ASY,140,169,0,Normal,140,N,0,Up,0
-53,M,ATA,120,181,0,Normal,132,N,0,Up,0
-48,F,ATA,133,308,0,ST,156,N,2,Up,0
-36,M,ATA,120,166,0,Normal,180,N,0,Up,0
-48,M,NAP,110,211,0,Normal,138,N,0,Up,0
-47,F,ATA,140,257,0,Normal,135,N,1,Up,0
-53,M,ASY,130,182,0,Normal,148,N,0,Up,0
-65,M,ASY,115,0,0,Normal,93,Y,0,Flat,1
-32,M,TA,95,0,1,Normal,127,N,0.7,Up,1
-61,M,ASY,105,0,1,Normal,110,Y,1.5,Up,1
-50,M,ASY,145,0,1,Normal,139,Y,0.7,Flat,1
-57,M,ASY,110,0,1,ST,131,Y,1.4,Up,1
-51,M,ASY,110,0,1,Normal,92,N,0,Flat,1
-47,M,ASY,110,0,1,ST,149,N,2.1,Up,1
-60,M,ASY,160,0,1,Normal,149,N,0.4,Flat,1
-55,M,ATA,140,0,0,ST,150,N,0.2,Up,0
-53,M,ASY,125,0,1,Normal,120,N,1.5,Up,1
-62,F,ASY,120,0,1,ST,123,Y,1.7,Down,1
-51,M,ASY,95,0,1,Normal,126,N,2.2,Flat,1
-51,F,ASY,120,0,1,Normal,127,Y,1.5,Up,1
-55,M,ASY,115,0,1,Normal,155,N,0.1,Flat,1
-53,M,ATA,130,0,0,ST,120,N,0.7,Down,0
-58,M,ASY,115,0,1,Normal,138,N,0.5,Up,1
-57,M,ASY,95,0,1,Normal,182,N,0.7,Down,1
-65,M,ASY,155,0,0,Normal,154,N,1,Up,0
-60,M,ASY,125,0,1,Normal,110,N,0.1,Up,1
-41,M,ASY,125,0,1,Normal,176,N,1.6,Up,1
-34,M,ASY,115,0,1,Normal,154,N,0.2,Up,1
-53,M,ASY,80,0,0,Normal,141,Y,2,Down,0
-74,M,ATA,145,0,1,ST,123,N,1.3,Up,1
-57,M,NAP,105,0,1,Normal,148,N,0.3,Flat,1
-56,M,ASY,140,0,1,Normal,121,Y,1.8,Up,1
-61,M,ASY,130,0,1,Normal,77,N,2.5,Flat,1
-68,M,ASY,145,0,1,Normal,136,N,1.8,Up,1
-59,M,NAP,125,0,1,Normal,175,N,2.6,Flat,1
-63,M,ASY,100,0,1,Normal,109,N,-0.9,Flat,1
-38,F,ASY,105,0,1,Normal,166,N,2.8,Up,1
-62,M,ASY,115,0,1,Normal,128,Y,2.5,Down,1
-46,M,ASY,100,0,1,ST,133,N,-2.6,Flat,1
-42,M,ASY,105,0,1,Normal,128,Y,-1.5,Down,1
-45,M,NAP,110,0,0,Normal,138,N,-0.1,Up,0
-59,M,ASY,125,0,1,Normal,119,Y,0.9,Up,1
-52,M,ASY,95,0,1,Normal,82,Y,0.8,Flat,1
-60,M,ASY,130,0,1,ST,130,Y,1.1,Down,1
-60,M,NAP,115,0,1,Normal,143,N,2.4,Up,1
-56,M,ASY,115,0,1,ST,82,N,-1,Up,1
-38,M,NAP,100,0,0,Normal,179,N,-1.1,Up,0
-40,M,ASY,95,0,1,ST,144,N,0,Up,1
-51,M,ASY,130,0,1,Normal,170,N,-0.7,Up,1
-62,M,TA,120,0,1,LVH,134,N,-0.8,Flat,1
-72,M,NAP,160,0,0,LVH,114,N,1.6,Flat,0
-63,M,ASY,150,0,1,ST,154,N,3.7,Up,1
-63,M,ASY,140,0,1,LVH,149,N,2,Up,1
-64,F,ASY,95,0,1,Normal,145,N,1.1,Down,1
-43,M,ASY,100,0,1,Normal,122,N,1.5,Down,1
-64,M,ASY,110,0,1,Normal,114,Y,1.3,Down,1
-61,M,ASY,110,0,1,Normal,113,N,1.4,Flat,1
-52,M,ASY,130,0,1,Normal,120,N,0,Flat,1
-51,M,ASY,120,0,1,Normal,104,N,0,Flat,1
-69,M,ASY,135,0,0,Normal,130,N,0,Flat,1
-59,M,ASY,120,0,0,Normal,115,N,0,Flat,1
-48,M,ASY,115,0,1,Normal,128,N,0,Flat,1
-69,M,ASY,137,0,0,ST,104,Y,1.6,Flat,1
-36,M,ASY,110,0,1,Normal,125,Y,1,Flat,1
-53,M,ASY,120,0,1,Normal,120,N,0,Flat,1
-43,M,ASY,140,0,0,ST,140,Y,0.5,Up,1
-56,M,ASY,120,0,0,ST,100,Y,-1,Down,1
-58,M,ASY,130,0,0,ST,100,Y,1,Flat,1
-55,M,ASY,120,0,0,ST,92,N,0.3,Up,1
-67,M,TA,145,0,0,LVH,125,N,0,Flat,1
-46,M,ASY,115,0,0,Normal,113,Y,1.5,Flat,1
-53,M,ATA,120,0,0,Normal,95,N,0,Flat,1
-38,M,NAP,115,0,0,Normal,128,Y,0,Flat,1
-53,M,NAP,105,0,0,Normal,115,N,0,Flat,1
-62,M,NAP,160,0,0,Normal,72,Y,0,Flat,1
-47,M,ASY,160,0,0,Normal,124,Y,0,Flat,1
-56,M,NAP,155,0,0,ST,99,N,0,Flat,1
-56,M,ASY,120,0,0,ST,148,N,0,Flat,1
-56,M,NAP,120,0,0,Normal,97,N,0,Flat,0
-64,F,ASY,200,0,0,Normal,140,Y,1,Flat,1
-61,M,ASY,150,0,0,Normal,117,Y,2,Flat,1
-68,M,ASY,135,0,0,ST,120,Y,0,Up,1
-57,M,ASY,140,0,0,Normal,120,Y,2,Flat,1
-63,M,ASY,150,0,0,Normal,86,Y,2,Flat,1
-60,M,ASY,135,0,0,Normal,63,Y,0.5,Up,1
-66,M,ASY,150,0,0,Normal,108,Y,2,Flat,1
-63,M,ASY,185,0,0,Normal,98,Y,0,Up,1
-59,M,ASY,135,0,0,Normal,115,Y,1,Flat,1
-61,M,ASY,125,0,0,Normal,105,Y,0,Down,1
-73,F,NAP,160,0,0,ST,121,N,0,Up,1
-47,M,NAP,155,0,0,Normal,118,Y,1,Flat,1
-65,M,ASY,160,0,1,ST,122,N,1.2,Flat,1
-70,M,ASY,140,0,1,Normal,157,Y,2,Flat,1
-50,M,ASY,120,0,0,ST,156,Y,0,Up,1
-60,M,ASY,160,0,0,ST,99,Y,0.5,Flat,1
-50,M,ASY,115,0,0,Normal,120,Y,0.5,Flat,1
-43,M,ASY,115,0,0,Normal,145,Y,2,Flat,1
-38,F,ASY,110,0,0,Normal,156,N,0,Flat,1
-54,M,ASY,120,0,0,Normal,155,N,0,Flat,1
-61,M,ASY,150,0,0,Normal,105,Y,0,Flat,1
-42,M,ASY,145,0,0,Normal,99,Y,0,Flat,1
-53,M,ASY,130,0,0,LVH,135,Y,1,Flat,1
-55,M,ASY,140,0,0,Normal,83,N,0,Flat,1
-61,M,ASY,160,0,1,ST,145,N,1,Flat,1
-51,M,ASY,140,0,0,Normal,60,N,0,Flat,1
-70,M,ASY,115,0,0,ST,92,Y,0,Flat,1
-61,M,ASY,130,0,0,LVH,115,N,0,Flat,1
-38,M,ASY,150,0,1,Normal,120,Y,0.7,Flat,1
-57,M,ASY,160,0,1,Normal,98,Y,2,Flat,1
-38,M,ASY,135,0,1,Normal,150,N,0,Flat,1
-62,F,TA,140,0,1,Normal,143,N,0,Flat,1
-58,M,ASY,170,0,1,ST,105,Y,0,Flat,1
-52,M,ASY,165,0,1,Normal,122,Y,1,Up,1
-61,M,NAP,200,0,1,ST,70,N,0,Flat,1
-50,F,ASY,160,0,1,Normal,110,N,0,Flat,1
-51,M,ASY,130,0,1,ST,163,N,0,Flat,1
-65,M,ASY,145,0,1,ST,67,N,0.7,Flat,1
-52,M,ASY,135,0,1,Normal,128,Y,2,Flat,1
-47,M,NAP,110,0,1,Normal,120,Y,0,Flat,1
-35,M,ASY,120,0,1,Normal,130,Y,1.2,Flat,1
-57,M,ASY,140,0,1,Normal,100,Y,0,Flat,1
-62,M,ASY,115,0,1,Normal,72,Y,-0.5,Flat,1
-59,M,ASY,110,0,1,Normal,94,N,0,Flat,1
-53,M,NAP,160,0,1,LVH,122,Y,0,Flat,1
-62,M,ASY,150,0,1,ST,78,N,2,Flat,1
-54,M,ASY,180,0,1,Normal,150,N,1.5,Flat,1
-56,M,ASY,125,0,1,Normal,103,Y,1,Flat,1
-56,M,NAP,125,0,1,Normal,98,N,-2,Flat,1
-54,M,ASY,130,0,1,Normal,110,Y,3,Flat,1
-66,F,ASY,155,0,1,Normal,90,N,0,Flat,1
-63,M,ASY,140,260,0,ST,112,Y,3,Flat,1
-44,M,ASY,130,209,0,ST,127,N,0,Up,0
-60,M,ASY,132,218,0,ST,140,Y,1.5,Down,1
-55,M,ASY,142,228,0,ST,149,Y,2.5,Up,1
-66,M,NAP,110,213,1,LVH,99,Y,1.3,Flat,0
-66,M,NAP,120,0,0,ST,120,N,-0.5,Up,0
-65,M,ASY,150,236,1,ST,105,Y,0,Flat,1
-60,M,NAP,180,0,0,ST,140,Y,1.5,Flat,0
-60,M,NAP,120,0,1,Normal,141,Y,2,Up,1
-60,M,ATA,160,267,1,ST,157,N,0.5,Flat,1
-56,M,ATA,126,166,0,ST,140,N,0,Up,0
-59,M,ASY,140,0,0,ST,117,Y,1,Flat,1
-62,M,ASY,110,0,0,Normal,120,Y,0.5,Flat,1
-63,M,NAP,133,0,0,LVH,120,Y,1,Flat,1
-57,M,ASY,128,0,1,ST,148,Y,1,Flat,1
-62,M,ASY,120,220,0,ST,86,N,0,Up,0
-63,M,ASY,170,177,0,Normal,84,Y,2.5,Down,1
-46,M,ASY,110,236,0,Normal,125,Y,2,Flat,1
-63,M,ASY,126,0,0,ST,120,N,1.5,Down,0
-60,M,ASY,152,0,0,ST,118,Y,0,Up,0
-58,M,ASY,116,0,0,Normal,124,N,1,Up,1
-64,M,ASY,120,0,1,ST,106,N,2,Flat,1
-63,M,NAP,130,0,0,ST,111,Y,0,Flat,1
-74,M,NAP,138,0,0,Normal,116,N,0.2,Up,0
-52,M,NAP,128,0,0,ST,180,N,3,Up,1
-69,M,ASY,130,0,1,ST,129,N,1,Flat,1
-51,M,ASY,128,0,1,ST,125,Y,1.2,Flat,1
-60,M,ASY,130,186,1,ST,140,Y,0.5,Flat,1
-56,M,ASY,120,100,0,Normal,120,Y,1.5,Flat,1
-55,M,NAP,136,228,0,ST,124,Y,1.6,Flat,1
-54,M,ASY,130,0,0,ST,117,Y,1.4,Flat,1
-77,M,ASY,124,171,0,ST,110,Y,2,Up,1
-63,M,ASY,160,230,1,Normal,105,Y,1,Flat,1
-55,M,NAP,0,0,0,Normal,155,N,1.5,Flat,1
-52,M,NAP,122,0,0,Normal,110,Y,2,Down,1
-64,M,ASY,144,0,0,ST,122,Y,1,Flat,1
-60,M,ASY,140,281,0,ST,118,Y,1.5,Flat,1
-60,M,ASY,120,0,0,Normal,133,Y,2,Up,0
-58,M,ASY,136,203,1,Normal,123,Y,1.2,Flat,1
-59,M,ASY,154,0,0,ST,131,Y,1.5,Up,0
-61,M,NAP,120,0,0,Normal,80,Y,0,Flat,1
-40,M,ASY,125,0,1,Normal,165,N,0,Flat,1
-61,M,ASY,134,0,1,ST,86,N,1.5,Flat,1
-41,M,ASY,104,0,0,ST,111,N,0,Up,0
-57,M,ASY,139,277,1,ST,118,Y,1.9,Flat,1
-63,M,ASY,136,0,0,Normal,84,Y,0,Flat,1
-59,M,ASY,122,233,0,Normal,117,Y,1.3,Down,1
-51,M,ASY,128,0,0,Normal,107,N,0,Up,0
-59,M,NAP,131,0,0,Normal,128,Y,2,Down,1
-42,M,NAP,134,240,0,Normal,160,N,0,Up,0
-55,M,NAP,120,0,0,ST,125,Y,2.5,Flat,1
-63,F,ATA,132,0,0,Normal,130,N,0.1,Up,0
-62,M,ASY,152,153,0,ST,97,Y,1.6,Up,1
-56,M,ATA,124,224,1,Normal,161,N,2,Flat,0
-53,M,ASY,126,0,0,Normal,106,N,0,Flat,1
-68,M,ASY,138,0,0,Normal,130,Y,3,Flat,1
-53,M,ASY,154,0,1,ST,140,Y,1.5,Flat,1
-60,M,NAP,141,316,1,ST,122,Y,1.7,Flat,1
-62,M,ATA,131,0,0,Normal,130,N,0.1,Up,0
-59,M,ASY,178,0,1,LVH,120,Y,0,Flat,1
-51,M,ASY,132,218,1,LVH,139,N,0.1,Up,0
-61,M,ASY,110,0,1,Normal,108,Y,2,Down,1
-57,M,ASY,130,311,1,ST,148,Y,2,Flat,1
-56,M,NAP,170,0,0,LVH,123,Y,2.5,Flat,1
-58,M,ATA,126,0,1,Normal,110,Y,2,Flat,1
-69,M,NAP,140,0,1,ST,118,N,2.5,Down,1
-67,M,TA,142,270,1,Normal,125,N,2.5,Up,1
-58,M,ASY,120,0,0,LVH,106,Y,1.5,Down,1
-65,M,ASY,134,0,0,Normal,112,Y,1.1,Flat,1
-63,M,ATA,139,217,1,ST,128,Y,1.2,Flat,1
-55,M,ATA,110,214,1,ST,180,N,0.4,Up,0
-57,M,ASY,140,214,0,ST,144,Y,2,Flat,1
-65,M,TA,140,252,0,Normal,135,N,0.3,Up,0
-54,M,ASY,136,220,0,Normal,140,Y,3,Flat,1
-72,M,NAP,120,214,0,Normal,102,Y,1,Flat,1
-75,M,ASY,170,203,1,ST,108,N,0,Flat,1
-49,M,TA,130,0,0,ST,145,N,3,Flat,1
-51,M,NAP,137,339,0,Normal,127,Y,1.7,Flat,1
-60,M,ASY,142,216,0,Normal,110,Y,2.5,Flat,1
-64,F,ASY,142,276,0,Normal,140,Y,1,Flat,1
-58,M,ASY,132,458,1,Normal,69,N,1,Down,0
-61,M,ASY,146,241,0,Normal,148,Y,3,Down,1
-67,M,ASY,160,384,1,ST,130,Y,0,Flat,1
-62,M,ASY,135,297,0,Normal,130,Y,1,Flat,1
-65,M,ASY,136,248,0,Normal,140,Y,4,Down,1
-63,M,ASY,130,308,0,Normal,138,Y,2,Flat,1
-69,M,ASY,140,208,0,ST,140,Y,2,Flat,1
-51,M,ASY,132,227,1,ST,138,N,0.2,Up,0
-62,M,ASY,158,210,1,Normal,112,Y,3,Down,1
-55,M,NAP,136,245,1,ST,131,Y,1.2,Flat,1
-75,M,ASY,136,225,0,Normal,112,Y,3,Flat,1
-40,M,NAP,106,240,0,Normal,80,Y,0,Up,0
-67,M,ASY,120,0,1,Normal,150,N,1.5,Down,1
-58,M,ASY,110,198,0,Normal,110,N,0,Flat,1
-60,M,ASY,136,195,0,Normal,126,N,0.3,Up,0
-63,M,ASY,160,267,1,ST,88,Y,2,Flat,1
-35,M,NAP,123,161,0,ST,153,N,-0.1,Up,0
-62,M,TA,112,258,0,ST,150,Y,1.3,Flat,1
-43,M,ASY,122,0,0,Normal,120,N,0.5,Up,1
-63,M,NAP,130,0,1,ST,160,N,3,Flat,0
-68,M,NAP,150,195,1,Normal,132,N,0,Flat,1
-65,M,ASY,150,235,0,Normal,120,Y,1.5,Flat,1
-48,M,NAP,102,0,1,ST,110,Y,1,Down,1
-63,M,ASY,96,305,0,ST,121,Y,1,Up,1
-64,M,ASY,130,223,0,ST,128,N,0.5,Flat,0
-61,M,ASY,120,282,0,ST,135,Y,4,Down,1
-50,M,ASY,144,349,0,LVH,120,Y,1,Up,1
-59,M,ASY,124,160,0,Normal,117,Y,1,Flat,1
-55,M,ASY,150,160,0,ST,150,N,0,Up,0
-45,M,NAP,130,236,0,Normal,144,N,0.1,Up,0
-65,M,ASY,144,312,0,LVH,113,Y,1.7,Flat,1
-61,M,ATA,139,283,0,Normal,135,N,0.3,Up,0
-49,M,NAP,131,142,0,Normal,127,Y,1.5,Flat,1
-72,M,ASY,143,211,0,Normal,109,Y,1.4,Flat,1
-50,M,ASY,133,218,0,Normal,128,Y,1.1,Flat,1
-64,M,ASY,143,306,1,ST,115,Y,1.8,Flat,1
-55,M,ASY,116,186,1,ST,102,N,0,Flat,1
-63,M,ASY,110,252,0,ST,140,Y,2,Flat,1
-59,M,ASY,125,222,0,Normal,135,Y,2.5,Down,1
-56,M,ASY,130,0,0,LVH,122,Y,1,Flat,1
-62,M,NAP,133,0,1,ST,119,Y,1.2,Flat,1
-74,M,ASY,150,258,1,ST,130,Y,4,Down,1
-54,M,ASY,130,202,1,Normal,112,Y,2,Flat,1
-57,M,ASY,110,197,0,LVH,100,N,0,Up,0
-62,M,NAP,138,204,0,ST,122,Y,1.2,Flat,1
-76,M,NAP,104,113,0,LVH,120,N,3.5,Down,1
-54,F,ASY,138,274,0,Normal,105,Y,1.5,Flat,1
-70,M,ASY,170,192,0,ST,129,Y,3,Down,1
-61,F,ATA,140,298,1,Normal,120,Y,0,Up,0
-48,M,ASY,132,272,0,ST,139,N,0.2,Up,0
-48,M,NAP,132,220,1,ST,162,N,0,Flat,1
-61,M,TA,142,200,1,ST,100,N,1.5,Down,1
-66,M,ASY,112,261,0,Normal,140,N,1.5,Up,1
-68,M,TA,139,181,1,ST,135,N,0.2,Up,0
-55,M,ASY,172,260,0,Normal,73,N,2,Flat,1
-62,M,NAP,120,220,0,LVH,86,N,0,Up,0
-71,M,NAP,144,221,0,Normal,108,Y,1.8,Flat,1
-74,M,TA,145,216,1,Normal,116,Y,1.8,Flat,1
-53,M,NAP,155,175,1,ST,160,N,0.3,Up,0
-58,M,NAP,150,219,0,ST,118,Y,0,Flat,1
-75,M,ASY,160,310,1,Normal,112,Y,2,Down,0
-56,M,NAP,137,208,1,ST,122,Y,1.8,Flat,1
-58,M,NAP,137,232,0,ST,124,Y,1.4,Flat,1
-64,M,ASY,134,273,0,Normal,102,Y,4,Down,1
-54,M,NAP,133,203,0,ST,137,N,0.2,Up,0
-54,M,ATA,132,182,0,ST,141,N,0.1,Up,0
-59,M,ASY,140,274,0,Normal,154,Y,2,Flat,0
-55,M,ASY,135,204,1,ST,126,Y,1.1,Flat,1
-57,M,ASY,144,270,1,ST,160,Y,2,Flat,1
-61,M,ASY,141,292,0,ST,115,Y,1.7,Flat,1
-41,M,ASY,150,171,0,Normal,128,Y,1.5,Flat,0
-71,M,ASY,130,221,0,ST,115,Y,0,Flat,1
-38,M,ASY,110,289,0,Normal,105,Y,1.5,Down,1
-55,M,ASY,158,217,0,Normal,110,Y,2.5,Flat,1
-56,M,ASY,128,223,0,ST,119,Y,2,Down,1
-69,M,ASY,140,110,1,Normal,109,Y,1.5,Flat,1
-64,M,ASY,150,193,0,ST,135,Y,0.5,Flat,1
-72,M,ASY,160,123,1,LVH,130,N,1.5,Flat,1
-69,M,ASY,142,210,1,ST,112,Y,1.5,Flat,1
-56,M,ASY,137,282,1,Normal,126,Y,1.2,Flat,1
-62,M,ASY,139,170,0,ST,120,Y,3,Flat,1
-67,M,ASY,146,369,0,Normal,110,Y,1.9,Flat,1
-57,M,ASY,156,173,0,LVH,119,Y,3,Down,1
-69,M,ASY,145,289,1,ST,110,Y,1.8,Flat,1
-51,M,ASY,131,152,1,LVH,130,Y,1,Flat,1
-48,M,ASY,140,208,0,Normal,159,Y,1.5,Up,1
-69,M,ASY,122,216,1,LVH,84,Y,0,Flat,1
-69,M,NAP,142,271,0,LVH,126,N,0.3,Up,0
-64,M,ASY,141,244,1,ST,116,Y,1.5,Flat,1
-57,M,ATA,180,285,1,ST,120,N,0.8,Flat,1
-53,M,ASY,124,243,0,Normal,122,Y,2,Flat,1
-37,M,NAP,118,240,0,LVH,165,N,1,Flat,0
-67,M,ASY,140,219,0,ST,122,Y,2,Flat,1
-74,M,NAP,140,237,1,Normal,94,N,0,Flat,1
-63,M,ATA,136,165,0,ST,133,N,0.2,Up,0
-58,M,ASY,100,213,0,ST,110,N,0,Up,0
-61,M,ASY,190,287,1,LVH,150,Y,2,Down,1
-64,M,ASY,130,258,1,LVH,130,N,0,Flat,1
-58,M,ASY,160,256,1,LVH,113,Y,1,Up,1
-60,M,ASY,130,186,1,LVH,140,Y,0.5,Flat,1
-57,M,ASY,122,264,0,LVH,100,N,0,Flat,1
-55,M,NAP,133,185,0,ST,136,N,0.2,Up,0
-55,M,ASY,120,226,0,LVH,127,Y,1.7,Down,1
-56,M,ASY,130,203,1,Normal,98,N,1.5,Flat,1
-57,M,ASY,130,207,0,ST,96,Y,1,Flat,0
-61,M,NAP,140,284,0,Normal,123,Y,1.3,Flat,1
-61,M,NAP,120,337,0,Normal,98,Y,0,Flat,1
-74,M,ASY,155,310,0,Normal,112,Y,1.5,Down,1
-68,M,NAP,134,254,1,Normal,151,Y,0,Up,0
-51,F,ASY,114,258,1,LVH,96,N,1,Up,0
-62,M,ASY,160,254,1,ST,108,Y,3,Flat,1
-53,M,ASY,144,300,1,ST,128,Y,1.5,Flat,1
-62,M,ASY,158,170,0,ST,138,Y,0,Flat,1
-46,M,ASY,134,310,0,Normal,126,N,0,Flat,1
-54,F,ASY,127,333,1,ST,154,N,0,Flat,1
-62,M,TA,135,139,0,ST,137,N,0.2,Up,0
-55,M,ASY,122,223,1,ST,100,N,0,Flat,1
-58,M,ASY,140,385,1,LVH,135,N,0.3,Up,0
-62,M,ATA,120,254,0,LVH,93,Y,0,Flat,1
-70,M,ASY,130,322,0,LVH,109,N,2.4,Flat,1
-67,F,NAP,115,564,0,LVH,160,N,1.6,Flat,0
-57,M,ATA,124,261,0,Normal,141,N,0.3,Up,1
-64,M,ASY,128,263,0,Normal,105,Y,0.2,Flat,0
-74,F,ATA,120,269,0,LVH,121,Y,0.2,Up,0
-65,M,ASY,120,177,0,Normal,140,N,0.4,Up,0
-56,M,NAP,130,256,1,LVH,142,Y,0.6,Flat,1
-59,M,ASY,110,239,0,LVH,142,Y,1.2,Flat,1
-60,M,ASY,140,293,0,LVH,170,N,1.2,Flat,1
-63,F,ASY,150,407,0,LVH,154,N,4,Flat,1
-59,M,ASY,135,234,0,Normal,161,N,0.5,Flat,0
-53,M,ASY,142,226,0,LVH,111,Y,0,Up,0
-44,M,NAP,140,235,0,LVH,180,N,0,Up,0
-61,M,TA,134,234,0,Normal,145,N,2.6,Flat,1
-57,F,ASY,128,303,0,LVH,159,N,0,Up,0
-71,F,ASY,112,149,0,Normal,125,N,1.6,Flat,0
-46,M,ASY,140,311,0,Normal,120,Y,1.8,Flat,1
-53,M,ASY,140,203,1,LVH,155,Y,3.1,Down,1
-64,M,TA,110,211,0,LVH,144,Y,1.8,Flat,0
-40,M,TA,140,199,0,Normal,178,Y,1.4,Up,0
-67,M,ASY,120,229,0,LVH,129,Y,2.6,Flat,1
-48,M,ATA,130,245,0,LVH,180,N,0.2,Flat,0
-43,M,ASY,115,303,0,Normal,181,N,1.2,Flat,0
-47,M,ASY,112,204,0,Normal,143,N,0.1,Up,0
-54,F,ATA,132,288,1,LVH,159,Y,0,Up,0
-48,F,NAP,130,275,0,Normal,139,N,0.2,Up,0
-46,F,ASY,138,243,0,LVH,152,Y,0,Flat,0
-51,F,NAP,120,295,0,LVH,157,N,0.6,Up,0
-58,M,NAP,112,230,0,LVH,165,N,2.5,Flat,1
-71,F,NAP,110,265,1,LVH,130,N,0,Up,0
-57,M,NAP,128,229,0,LVH,150,N,0.4,Flat,1
-66,M,ASY,160,228,0,LVH,138,N,2.3,Up,0
-37,F,NAP,120,215,0,Normal,170,N,0,Up,0
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-42,M,ASY,136,315,0,Normal,125,Y,1.8,Flat,1
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-39,F,NAP,94,199,0,Normal,179,N,0,Up,0
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-41,M,NAP,130,214,0,LVH,168,N,2,Flat,0
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-42,M,ASY,140,226,0,Normal,178,N,0,Up,0
-64,M,ASY,120,246,0,LVH,96,Y,2.2,Down,1
-54,M,NAP,150,232,0,LVH,165,N,1.6,Up,0
-46,F,NAP,142,177,0,LVH,160,Y,1.4,Down,0
-67,F,NAP,152,277,0,Normal,172,N,0,Up,0
-56,M,ASY,125,249,1,LVH,144,Y,1.2,Flat,1
-34,F,ATA,118,210,0,Normal,192,N,0.7,Up,0
-57,M,ASY,132,207,0,Normal,168,Y,0,Up,0
-64,M,ASY,145,212,0,LVH,132,N,2,Flat,1
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-63,F,NAP,135,252,0,LVH,172,N,0,Up,0
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-71,F,ATA,160,302,0,Normal,162,N,0.4,Up,0
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-55,M,ASY,132,353,0,Normal,132,Y,1.2,Flat,1
-64,M,NAP,140,335,0,Normal,158,N,0,Up,1
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-49,M,ATA,130,266,0,Normal,171,N,0.6,Up,0
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-52,M,NAP,172,199,1,Normal,162,N,0.5,Up,0
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-53,F,NAP,128,216,0,LVH,115,N,0,Up,0
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-52,M,ASY,128,204,1,Normal,156,Y,1,Flat,1
-59,M,TA,134,204,0,Normal,162,N,0.8,Up,1
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-66,F,NAP,146,278,0,LVH,152,N,0,Flat,0
-39,F,NAP,138,220,0,Normal,152,N,0,Flat,0
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-47,M,NAP,130,253,0,Normal,179,N,0,Up,0
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-35,M,ATA,122,192,0,Normal,174,N,0,Up,0
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-67,M,NAP,152,212,0,LVH,150,N,0.8,Flat,1
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-63,M,ASY,140,187,0,LVH,144,Y,4,Up,1
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models.pkl

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ratios.pkl

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requirements.txt

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-pandas
-numpy
-matplotlib
-seaborn
-gradio
-scikit-learn
-xgboost
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+pandas==2.0.3
+numpy==1.24.3
+scikit-learn==1.3.0
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