Maternal health risk prediction using machine learning

Maternal health Risk Prediction/app.py

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+import streamlit as st
+import joblib
+import pandas as pd
+
+# Load the trained XGBoost model
+model_xgb = joblib.load('xgb_model.pkl')
+
+# Function to make predictions
+def predict_risk_level(age, systolic_bp, diastolic_bp, bs, body_temp, heart_rate):
+    # Create a DataFrame for the input data
+    input_data = pd.DataFrame({
+        'Age': [age],
+        'SystolicBP': [systolic_bp],
+        'DiastolicBP': [diastolic_bp],
+        'BS': [bs],
+        'BodyTemp': [body_temp],
+        'HeartRate': [heart_rate]
+    })
+
+    # Predict using the loaded model
+    prediction_proba = model_xgb.predict_proba(input_data)[0]
+
+    # Determine risk level based on probability thresholds
+    low_risk_threshold = 0.33
+    mid_risk_threshold = 0.66
+
+    if prediction_proba[2] > mid_risk_threshold:
+        risk_level = 'High Maternal risk'
+    elif prediction_proba[1] > low_risk_threshold:
+        risk_level = 'Medium Maternal risk'
+    else:
+        risk_level = 'Low Maternal risk'
+
+    return risk_level
+
+# Streamlit app interface
+st.set_page_config(page_title="Maternal Risk Prediction", page_icon=":baby:", layout="wide")
+
+# Add a maternal-themed image as background with blur
+st.markdown("""
+    <style>
+    .title {
+        text-align: center;
+        color: #FF6347;
+        font-size: 36px;
+        font-weight: bold;
+        margin-top: 20px;
+    }
+    .subtitle {
+        color: #4682B4;
+        font-size: 28px;
+        font-weight: bold;
+    }
+    .input-container {
+        border: 2px solid #4682B4;
+        border-radius: 10px;
+        padding: 20px;
+        background-color: rgba(240, 248, 255, 0.8); /* Semi-transparent background */
+    }
+    .output-container {
+        border: 2px solid #4682B4;
+        border-radius: 10px;
+        padding: 20px;
+        background-color: rgba(240, 248, 255, 0.8); /* Semi-transparent background */
+    }
+    .img-container {
+        text-align: center;
+        margin-bottom: 20px;
+    }
+    .img-container img {
+        width: 100%;
+        max-width: 1000px; /* Adjust max-width as needed */
+        height: auto;
+        border-radius: 10px;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Background image
+st.markdown("<div class='background'></div>", unsafe_allow_html=True)
+
+# Main title
+st.markdown("<div class='title'>Maternal Risk Prediction</div>", unsafe_allow_html=True)
+
+
+# Input container with styling
+with st.container():
+    st.markdown("<div class='subtitle'>Enter the details below:</div>", unsafe_allow_html=True)
+
+    with st.container():
+        col1, col2 = st.columns(2)
+
+        with col1:
+            age = st.slider('Age', min_value=10, max_value=100, value=30, key='age')
+            systolic_bp = st.slider('Systolic BP', min_value=70, max_value=200, value=120, key='systolic_bp')
+            diastolic_bp = st.slider('Diastolic BP', min_value=50, max_value=120, value=80, key='diastolic_bp')
+            bs = st.slider('Blood Sugar', min_value=5.0, max_value=20.0, value=7.0, format="%.1f", key='bs')
+
+        with col2:
+            body_temp = st.slider('Body Temperature (Fahrenheit)', min_value=95.0, max_value=105.0, value=98.6,
+                                  format="%.1f", key='body_temp')
+            heart_rate = st.slider('Heart Rate', min_value=50, max_value=150, value=80, key='heart_rate')
+
+        st.markdown("<hr>", unsafe_allow_html=True)
+
+        # Prediction button
+        if st.button('Predict Risk Level'):
+            risk_level = predict_risk_level(age, systolic_bp, diastolic_bp, bs, body_temp, heart_rate)
+
+            # Display the prediction with proper styling
+            with st.container():
+                if risk_level == 'Low Maternal risk':
+                    st.success(f'**Predicted Risk Level:** {risk_level.upper()}')
+                elif risk_level == 'Medium Maternal risk':
+                    st.warning(f'**Predicted Risk Level:** {risk_level.upper()}')
+                elif risk_level == 'High Maternal risk':
+                    st.error(f'**Predicted Risk Level:** {risk_level.upper()}')