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Final Model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:730ddac99ed50df8bb7a95c207a1df019c3855f9067bbd26ef32b4ea656506bc
+size 139576

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from joblib import load
+import os
+import sys
+
+# Get current directory where model files are located
+current_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Set page config
+st.set_page_config(
+    page_title="Healthcare Stroke Prediction System",
+    page_icon="⚕️",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS
+st.markdown("""
+    <style>
+        .main {
+            padding-top: 2rem;
+        }
+        .prediction-box {
+            padding: 2rem;
+            border-radius: 10px;
+            margin-top: 2rem;
+        }
+        .high-risk {
+            background-color: #ffebee;
+            border-left: 4px solid #f44336;
+        }
+        .low-risk {
+            background-color: #e8f5e9;
+            border-left: 4px solid #4caf50;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# Title and header
+st.title("⚕️ Stroke Prediction System")
+st.markdown("---")
+st.markdown("**Predict stroke risk based on patient health data using Machine Learning**")
+
+# Load model and preprocessing objects
+try:
+    model = load("Final Model.pkl")
+    scaler = load("scaler.pkl")
+    columns = load("columns.pkl")
+except FileNotFoundError:
+    st.error("❌ Model files not found. Please ensure 'Final Model.pkl', 'scaler.pkl', and 'columns.pkl' are in the parent directory.")
+    st.stop()
+
+# Create two columns for input
+col1, col2 = st.columns(2)
+
+with col1:
+    st.subheader("👤 Personal Information")
+    age = st.slider("Age", min_value=18, max_value=100, value=45, step=1)
+    gender = st.selectbox("Gender", ["Male", "Female", "Other"])
+    ever_married = st.selectbox("Ever Married", ["No", "Yes"])
+    
+    st.subheader("💼 Work & Residence")
+    work_type = st.selectbox("Work Type", ["Private", "Self-employed", "Govt_job", "Never_worked", "children"])
+    residence_type = st.selectbox("Residence Type", ["Urban", "Rural"])
+
+with col2:
+    st.subheader("🏥 Health Metrics")
+    avg_glucose_level = st.number_input("Average Glucose Level (mg/dL)", min_value=50.0, max_value=300.0, value=120.0, step=1.0)
+    bmi = st.number_input("Body Mass Index (BMI)", min_value=10.0, max_value=60.0, value=25.0, step=0.1)
+    hypertension = st.selectbox("Hypertension", ["No", "Yes"])
+    heart_disease = st.selectbox("Heart Disease", ["No", "Yes"])
+    
+    st.subheader("🚬 Lifestyle")
+    smoking_status = st.selectbox("Smoking Status", ["never smoked", "formerly smoked", "smokes", "Unknown"])
+
+# Encode categorical variables
+gender_map = {"Female": 0, "Male": 1, "Other": 2}
+work_map = {"Private": 0, "Self-employed": 1, "Govt_job": 2, "Never_worked": 3, "children": 4}
+residence_map = {"Rural": 0, "Urban": 1}
+smoking_map = {"never smoked": 0, "formerly smoked": 1, "smokes": 2, "Unknown": 3}
+married_map = {"No": 0, "Yes": 1}
+condition_map = {"No": 0, "Yes": 1}
+
+# Prepare for scaling (scale only the numeric features)
+numeric_features = np.array([[age, avg_glucose_level, bmi]])
+scaled_features = scaler.transform(numeric_features)[0]
+
+# Create final input for model (in correct order: gender, age, hypertension, heart_disease, ever_married, work_type, Residence_type, avg_glucose_level, bmi, smoking_status)
+final_input = np.array([
+    gender_map[gender],
+    scaled_features[0],  # scaled age
+    condition_map[hypertension],
+    condition_map[heart_disease],
+    married_map[ever_married],
+    work_map[work_type],
+    residence_map[residence_type],
+    scaled_features[1],  # scaled avg_glucose_level
+    scaled_features[2],  # scaled bmi
+    smoking_map[smoking_status]
+]).reshape(1, -1)
+
+# Prediction button and results
+st.markdown("---")
+col_btn1, col_btn2, col_btn3 = st.columns([1, 1, 2])
+
+with col_btn1:
+    if st.button("🔮 Predict Stroke Risk", use_container_width=True):
+        # Make prediction
+        prediction = model.predict(final_input)[0]
+        probability = model.predict_proba(final_input)[0]
+        
+        # Store in session state
+        st.session_state.prediction = prediction
+        st.session_state.probability = probability
+
+with col_btn2:
+    if st.button("🔄 Reset Form", use_container_width=True):
+        st.rerun()
+
+# Display results
+if "prediction" in st.session_state:
+    prediction = st.session_state.prediction
+    probability = st.session_state.probability
+    
+    st.markdown("---")
+    st.subheader("📊 Prediction Results")
+    
+    # Create result display
+    if prediction == 1:
+        risk_level = "HIGH RISK"
+        risk_class = "high-risk"
+        risk_color = "🔴"
+        recommendation = "⚠️ **Please consult with a healthcare professional immediately for further evaluation and preventive measures.**"
+    else:
+        risk_level = "LOW RISK"
+        risk_class = "low-risk"
+        risk_color = "🟢"
+        recommendation = "✅ **Continue maintaining healthy lifestyle habits. Regular check-ups are recommended.**"
+    
+    # Display prediction box
+    col_result1, col_result2 = st.columns(2)
+    
+    with col_result1:
+        st.markdown(f"""
+            <div class="prediction-box {risk_class}">
+                <h2>{risk_color} {risk_level}</h2>
+                <p><strong>Stroke Risk Probability:</strong></p>
+                <h3>{probability[1]*100:.2f}%</h3>
+            </div>
+        """, unsafe_allow_html=True)
+    
+    with col_result2:
+        st.markdown(f"""
+            <div class="prediction-box {risk_class}">
+                <h4>Recommendation</h4>
+                <p>{recommendation}</p>
+            </div>
+        """, unsafe_allow_html=True)
+    
+    # Detailed breakdown
+    st.subheader("📈 Probability Breakdown")
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.metric("Low Risk Probability", f"{probability[0]*100:.2f}%")
+    with col2:
+        st.metric("High Risk Probability", f"{probability[1]*100:.2f}%")
+    
+    # Risk factors summary
+    st.subheader("📋 Patient Summary")
+    summary_df = pd.DataFrame({
+        "Parameter": ["Age", "Gender", "Average Glucose Level", "BMI", "Work Type", "Smoking Status", "Marital Status", "Residence Type"],
+        "Value": [age, gender, f"{avg_glucose_level} mg/dL", f"{bmi}", work_type, smoking_status, ever_married, residence_type]
+    })
+    st.table(summary_df)
+
+# Footer
+st.markdown("---")
+st.markdown("""
+    <div style="text-align: center; color: gray; font-size: 0.85rem;">
+        <p>⚕️ <strong>Disclaimer:</strong> This is an AI-based prediction tool for educational and awareness purposes only. 
+        It should not be used as a substitute for professional medical advice. Always consult a healthcare provider for medical decisions.</p>
+        <p>Model: Gradient Boosting Classifier | Data: Healthcare Stroke Dataset</p>
+    </div>
+""", unsafe_allow_html=True)
+
+

columns.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cfcf569aab73925d6a434b7e32fcb4f2a80ceaecee556571cabd2b6a1a562067
+size 149

requirements.txt

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+numpy>=1.22.0
+pandas>=1.4.0
+scikit-learn>=1.0.0
+xgboost>=1.5.0
+matplotlib>=3.5.0
+seaborn>=0.11.0
+streamlit>=1.0.0
+joblib

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ba916a69082b99174c0881a039cb83abe8d88520c06047ff3a3384cdc2803c71
+size 959