Duplicate from bharat10/heart_disease_prediction

Co-authored-by: Bharat Uttamchandani <bharat10@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+title: Heart Disease Prediction
+emoji: 👁
+colorFrom: gray
+colorTo: purple
+sdk: gradio
+sdk_version: 3.19.1
+app_file: app.py
+pinned: false
+duplicated_from: bharat10/heart_disease_prediction
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+from sklearn.metrics import confusion_matrix
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import classification_report
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.svm import SVC
+from sklearn.model_selection import RandomizedSearchCV
+from sklearn.preprocessing import StandardScaler
+import matplotlib.pyplot as plt
+# %matplotlib inline
+import io
+
+def importdata():
+		#balance_data = pd.read_csv(io.BytesIO(uploaded['heart_disease_data.csv']))
+			balance_data = pd.read_csv('heart_disease_data.csv')
+		# Printing the dataswet shape
+			print ("Dataset Length: ", len(balance_data))
+			print ("Dataset Shape: ", balance_data.shape)
+		
+		# Printing the dataset obseravtions
+			print ("Dataset: ",balance_data.head())
+		
+			return balance_data
+def splitdatasetL(heart_data, input_data):
+  X = heart_data.drop(columns='target', axis=1)
+  Y = heart_data['target']
+  X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)
+  model = LogisticRegression()
+  model.fit(X_train, Y_train)
+
+  input_data_as_numpy_array= np.asarray(input_data)
+
+  # reshape the numpy array as we are predicting for only on instance
+  input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)
+
+  prediction = model.predict(input_data_reshaped)
+
+  return prediction[0]
+def splitdataset(balance_data):
+
+	# Separating the target variable
+	X = balance_data.values[:, 0:13]
+	Y = balance_data.values[:, 13]
+
+	# Splitting the dataset into train and test
+	X_train, X_test, y_train, y_test = train_test_split(
+	X, Y, test_size = 0.3, random_state = 100)
+	
+	return X, Y, X_train, X_test, y_train, y_test
+def train_using_gini(X_train, X_test, y_train):
+
+	clf_gini = DecisionTreeClassifier(criterion = "gini",
+			random_state = 100,max_depth=3, min_samples_leaf=5)
+
+	clf_gini.fit(X_train, y_train)
+	return clf_gini
+
+def tarin_using_entropy(X_train, X_test, y_train):
+
+	clf_entropy = DecisionTreeClassifier(
+			criterion = "entropy", random_state = 100,
+			max_depth = 3, min_samples_leaf = 5)
+
+	clf_entropy.fit(X_train, y_train)
+	return clf_entropy
+
+
+# Function to make predictions
+def prediction(X_test, clf_object):
+
+	# Predicton on test with giniIndex
+	y_pred = clf_object.predict(X_test)
+	print("Predicted values:")
+	print(y_pred)
+	return y_pred
+def RandomF(X_train, y_train, X_test):
+  rf_clf = RandomForestClassifier(n_estimators=1000, random_state=42)
+  rf_clf.fit(X_train, y_train)
+  pred = rf_clf.predict(X_test)
+  return pred
+def SBM(df, X_test):
+  X = df.drop('target', axis=1)
+  y = df['target']
+  X_train, X_T, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+  scaler = StandardScaler()
+  X_train_scaled = scaler.fit_transform(X_train)
+  X_test_scaled = scaler.fit_transform(X_test)
+  svm = SVC(kernel='rbf', gamma=0.1)
+  svm.fit(X_train_scaled, y_train)
+  y_pred = svm.predict(X_test_scaled)
+  return y_pred
+
+def SBF(new_data):
+  df = pd.read_csv('heart_disease_data.csv')
+  X = df.drop('target', axis=1)
+  y = df['target']
+  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=5)
+  svm = SVC(kernel='linear')
+  svm.fit(X_train, y_train)
+  y_pred = svm.predict(new_data)
+  print(y_pred)
+  print(y_pred[0])
+  print("MEasdasdaGASDASD")
+  return y_pred[0]
+
+def heart(age, gender, chestpaintype, restingbloodpressure, serumcholestrol, fastingbloodsugar, resting_ecg_result, maximumheartrate, exerciseinduced_angina, oldpeak, slope, ca, thal):
+    data = importdata()
+    X, Y, X_train, X_test, y_train, y_test = splitdataset(data)
+    clf_gini = train_using_gini(X_train, X_test, y_train)
+    clf_entropy = tarin_using_entropy(X_train, X_test, y_train)
+    
+    fbs = 1 if fastingbloodsugar > 120 else 0
+    g   = 0 if gender == "Female" else 1
+    exang   = 0 if exerciseinduced_angina == "No" else 1
+    cp = 0
+    if chestpaintype == "Typical Angina" :
+      cp = 0
+    elif chestpaintype == "Non Typical Angina" :
+      cp = 1
+    elif chestpaintype == "Non Anginal Pain" :
+      cp = 2
+    else :
+      cp = 3
+    ecg = 0
+    if resting_ecg_result == "0 - Nothing to note" :
+      ecg = 0
+    elif resting_ecg_result == "1 - ST-T abnormality" :
+      ecg = 1
+    else :
+      ecg = 2  
+    
+    XX = np.array([age, g, cp, restingbloodpressure, serumcholestrol, fbs, ecg, maximumheartrate, exang, oldpeak, slope, ca, thal])
+    X_test[1][0] = age
+    X_test[1][1] = g
+    X_test[1][2] = cp
+    X_test[1][3] = restingbloodpressure
+    X_test[1][4] = serumcholestrol
+    X_test[1][5] = fbs
+    X_test[1][6] = ecg
+    X_test[1][7] = maximumheartrate
+    X_test[1][8] = exang
+    X_test[1][9] = oldpeak
+    X_test[1][10] = slope
+    X_test[1][11] = ca
+    X_test[1][12] = thal
+    new_data = pd.DataFrame({'age':[age],'sex':[g],'cp':[cp],'trestbps':[restingbloodpressure],
+                         'chol': [serumcholestrol],'fbs':[fbs],'restecg': [ecg],
+                         'thalach':[maximumheartrate],'exang':[exang],'oldpeak': [oldpeak],
+                         'slope':[slope],	'ca':[ca],	'thal':[thal]})
+    y_pred_gini = prediction(X_test, clf_gini)
+    k = RandomF(X_train, y_train, X_test)
+    #m = SBM(data, new_data)
+    m = SBF(new_data)
+    print("ASDASDASDADS")
+    print(type(m))
+    #m = 0
+    pred = splitdatasetL(data, XX)
+    if y_pred_gini[1] == 1.0:
+      SD = "Based on our Decision Tree Machine Learning model which has an accuracy of 82.42%, you have high chances of having heart disease"
+    else:
+      SD = "Based on our Decision Tree Machine Learning model which has an accuracy of 82.42%, you are less likely to have heart disease"
+    if pred == 1:
+      SL = "Based on our Logistic Regression Machine Learning model which has an accuracy of 81.97%, you have high chances of having heart disease"
+    else:
+      SL = "Based on our Logistic Regression Machine Learning model which has an accuracy of 81.97%, you are less likely to have heart disease"
+    if k[1] == 1:
+      SR = "Based on our Random Forest Machine Learning model which has an accuracy of 82.42%, you have high chances of having heart disease"
+    else:
+      SR = "Based on our Random Forest Machine Learning model which has an accuracy of 82.42%, you are less likely to have heart disease"
+    if m == 1:
+      SS = "Based on our SVM Machine Learning model which has an accuracy of 89.01%, you have high chances of having heart disease"
+    else:
+      SS = "Based on our SVM Machine Learning model which has an accuracy of 89.01%, you are less likely to have heart disease"
+    
+    models = ['Logistic Regression','Decision Tree','SVM','Random Forest']
+    accuracies = [81.97,82.42,89.01,82.42]
+
+    fig, ax = plt.subplots(figsize = (40,40))
+    ax.bar(models, accuracies)
+    ax.set_xlabel('Models')
+    ax.set_ylabel('Accuracy')
+    ax.set_title('Machine Learning Models Accuracy')
+
+    return SL, SD, SS, SR, fig
+
+interface = gr.Interface(
+    fn=heart,
+    inputs=["number", gr.Radio(["Male", "Female"]),gr.Dropdown(["Typical Angina", "Non Typical Angina", "Non Anginal Pain", "Asymptomatic"]), "number", "number", "number", gr.Dropdown(["0 - Nothing to note", "1 - ST-T abnormality", "2 - Possible or definite left ventricular hypertrophy"]), "number", gr.Radio(["No", "Yes"]), "number" , "number", "number", "number"],
+    outputs=[gr.outputs.Label(label="Logistic Regression", type="text"),gr.outputs.Label(label="Decision Tree", type="auto"),gr.outputs.Label(label="Random Forest", type="text"),gr.outputs.Label(label="SVM", type="auto"),"plot"],
+
+)
+
+interface.launch()

heart_disease_data.csv

@@ -0,0 +1,304 @@
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requirements.txt

@@ -0,0 +1 @@
+scikit-learn