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feature_columns.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:420aef88aed5ca5dddb4cba12386095b1a01c790a956e08f2df64627f0987830
+size 425

label_encoders.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd4171507eeb3a2a2a03232dd78213690254c02f629501e3dae519367c430a7e
+size 2074

label_encoders_2.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9f3a92a7bdd1f3c46683a18dab5bad15045ae1168fd8ae697a26c74d0278886d
+size 1851

lgbm_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:71f2545e6a4b6f652c0141ca199fb5971c6c0babb30ddc9286b1e79b30627d75
+size 681956

rf_tuned_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ad7fcd8e2055b459da82bf58ce958877d4c03ad382545103909cb97b4d57a7db
+size 11916921

web.py

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+import streamlit as st
+import pandas as pd
+import joblib
+import numpy as np
+
+# Load trained models
+rf_model = joblib.load("rf_tuned_model.pkl")
+xgb_model = joblib.load("xgb_model.pkl")
+lgbm_model = joblib.load("lgbm_model.pkl")
+
+# Load encoders and feature order
+label_encoders = joblib.load("label_encoders.pkl")
+label_encoders_2 = joblib.load("label_encoders_2.pkl")
+feature_order = joblib.load("feature_columns.pkl")
+
+st.title("🏥 Hospital Readmission Prediction App")
+
+st.sidebar.header("📋 Enter Patient Information")
+
+# User input fields
+time_in_hospital = st.sidebar.number_input("Days in Hospital", min_value=1, max_value=30, step=1)
+n_lab_procedures = st.sidebar.number_input("Number of Lab Procedures", min_value=0, max_value=100, step=1)
+n_procedures = st.sidebar.number_input("Number of Procedures", min_value=0, max_value=10, step=1)
+n_medications = st.sidebar.number_input("Number of Medications", min_value=0, max_value=50, step=1)
+age = st.sidebar.selectbox("Age Group", ["[0-10)", "[10-20)", "[20-30)", "[30-40)", "[40-50)", "[50-60)", "[60-70)", "[70-80)", "[80-90)", "[90-100)"])
+glucose_test = st.sidebar.selectbox("Glucose Test", ["Yes", "No"])
+A1Ctest = st.sidebar.selectbox("A1C Test", ["Yes", "No"])
+change = st.sidebar.selectbox("Change in Medication", ["Yes", "No"])
+diabetes_med = st.sidebar.selectbox("Diabetes Medication", ["Yes", "No"])
+
+# Convert user input into a DataFrame
+user_input_df = pd.DataFrame({
+    "time_in_hospital": [time_in_hospital],
+    "n_lab_procedures": [n_lab_procedures],
+    "n_procedures": [n_procedures],
+    "n_medications": [n_medications],
+    "age": [age],
+    "glucose_test": [glucose_test],
+    "A1Ctest": [A1Ctest],
+    "change": [change],
+    "diabetes_med": [diabetes_med]
+})
+
+# Encode categorical features
+for col in label_encoders:
+    if col in user_input_df:
+        user_input_df[col] = user_input_df[col].apply(lambda x: x if x in label_encoders[col].classes_ else "Unknown")
+        label_encoders[col].classes_ = np.append(label_encoders[col].classes_, "Unknown")
+        user_input_df[col] = label_encoders[col].transform(user_input_df[col])
+
+# Ensure the feature order matches the training data
+user_input_df = user_input_df.reindex(columns=feature_order, fill_value=0)
+
+# Predict using the models
+if st.sidebar.button("🔍 Predict Readmission"):
+    rf_prediction = rf_model.predict(user_input_df)[0]
+    rf_proba = rf_model.predict_proba(user_input_df)[0][1]
+
+    xgb_prediction = xgb_model.predict(user_input_df)[0]
+    xgb_proba = xgb_model.predict_proba(user_input_df)[0][1]
+
+    lgbm_prediction = lgbm_model.predict(user_input_df)[0]
+    lgbm_proba = lgbm_model.predict_proba(user_input_df)[0][1]
+
+    # Display Results
+    st.subheader("📊 Prediction Results")
+
+    def format_prediction(pred, proba):
+        if pred == 0:
+            return f"🔴 **Likely to be Readmitted** (Probability: {proba:.2%})"
+        else:
+            return f"🟢 **Not Likely to be Readmitted** (Probability: {proba:.2%})"
+
+    st.write("**Random Forest:**", format_prediction(rf_prediction, rf_proba))
+    st.write("**XGBoost:**", format_prediction(xgb_prediction, xgb_proba))
+    st.write("**LightGBM:**", format_prediction(lgbm_prediction, lgbm_proba))
+    
+    # Choose final prediction (majority vote)
+    final_prediction = round((rf_prediction + xgb_prediction + lgbm_prediction) / 3)
+    final_proba = (rf_proba + xgb_proba + lgbm_proba) / 3
+
+    st.markdown("### 🏥 **Final Prediction:**")
+    st.write(format_prediction(final_prediction, final_proba))

xgb_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eba428f342bfd9bd713c84d2cd3b2fd12d7fca47810eec2acb6692282480f759
+size 3404829