File size: 3,097 Bytes
e8ea1d6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | import streamlit as st
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
# Set page config
st.set_page_config(
page_title="Diabetes Detection App",
page_icon="🏥",
layout="wide"
)
# Title
st.title("Diabetes Detection App")
st.write("This app predicts diabetes using various health metrics.")
# Load data
@st.cache_data
def load_data():
# Load the Pima Indians Diabetes Database
columns = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness',
'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome']
data = pd.read_csv('https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv',
names=columns)
return data
# Load and prepare data
data = load_data()
# Sidebar for user input
st.sidebar.header('User Input Features')
def user_input_features():
pregnancies = st.sidebar.slider('Pregnancies', 0, 17, 3)
glucose = st.sidebar.slider('Glucose', 0, 200, 120)
blood_pressure = st.sidebar.slider('Blood Pressure', 0, 122, 70)
skin_thickness = st.sidebar.slider('Skin Thickness', 0, 100, 20)
insulin = st.sidebar.slider('Insulin', 0, 846, 79)
bmi = st.sidebar.slider('BMI', 0.0, 67.1, 31.4)
diabetes_pedigree = st.sidebar.slider('Diabetes Pedigree Function', 0.078, 2.42, 0.3725)
age = st.sidebar.slider('Age', 21, 81, 29)
data = {
'Pregnancies': pregnancies,
'Glucose': glucose,
'BloodPressure': blood_pressure,
'SkinThickness': skin_thickness,
'Insulin': insulin,
'BMI': bmi,
'DiabetesPedigreeFunction': diabetes_pedigree,
'Age': age
}
return pd.DataFrame(data, index=[0])
# Get user input
user_data = user_input_features()
# Display user input
st.subheader('User Input Features')
st.write(user_data)
# Prepare the model
X = data.drop('Outcome', axis=1)
y = data['Outcome']
# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Scale the features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Train the model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train_scaled, y_train)
# Make prediction on user input
user_data_scaled = scaler.transform(user_data)
prediction = model.predict(user_data_scaled)
prediction_proba = model.predict_proba(user_data_scaled)
# Show prediction
st.subheader('Prediction')
if prediction[0] == 0:
st.write('The model predicts: No Diabetes')
else:
st.write('The model predicts: Diabetes')
# Feature importance
st.subheader('Feature Importance')
feature_importance = pd.DataFrame({
'Feature': X.columns,
'Importance': model.feature_importances_
}).sort_values('Importance', ascending=False)
st.bar_chart(feature_importance.set_index('Feature')) |