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app.py

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+import streamlit as st
+import pandas as pd
+import pickle
+
+
+# Title
+st.write(""" 
+# DEATH PREDICTION APP
+
+This app predicts the likelihood of a patient's **Death** .
+
+""")
+
+@st.cache_data
+def fetch_data():
+    df = pd.read_csv('h8dsft_P1G3_AlselloDM.csv')
+    return df
+
+df = fetch_data()
+
+st.sidebar.title("Patient's Medical Records")
+st.sidebar.write('Please enter the condition here')
+st.sidebar.title("Prediction Form📋")
+
+def user_input_features():
+    # Age of the patient
+    age = st.sidebar.number_input("Age of the patient", 1,100)
+    # Decrease of red blood cells or hemoglobin
+    anaemia = st.sidebar.selectbox('Have Anaemia? (1 : True | 0 : False)',(1,0))
+    # Level of the CPK enzyme in the blood
+    creatinine_phosphokinase = st.sidebar.slider('How much is the Creatinine_Phosphokinase(CP) in the body?',20,8000,3000)
+    # If the patient has diabetes
+    diabetes = st.sidebar.selectbox('If the patient has diabetes (1 : True | 0 : False)',(1,0))
+    # Heart capability
+    ejection_fraction = st.sidebar.slider('What is the Ejection Fraction?',0,150,75)
+    # If the patient has hypertension
+    high_blood_pressure = st.sidebar.selectbox('If the patient has hypertension (1 : True | 0 : False)',(1,0))
+    # Platelets in the blood
+    platelets = st.sidebar.number_input("What is the Blood Platelets count? (kiloplatelets/mL)",0.0,100000.0)
+    # Level of creatinine in the blood
+    serum_creatinine = st.sidebar.slider('What is the level of serum creatinine in the bloodstream?',0.5,10.0,0.5)
+    # Level of sodium in the blood
+    serum_sodium = st.sidebar.slider('What is the level of Serum_Sodium in the Body?',50,200,50)
+    # Male or Female
+    sex = st.sidebar.selectbox('Gender',('Male','Female'))
+    # If the patient smokes
+    smoking = st.sidebar.selectbox('If the patient smokes (1 : True | 0 : False)',(1,0))
+    # Follow-up period
+    time = st.sidebar.slider('Follow-up period',0,400,20)
+    data = {'age': age,'anaemia':anaemia,'creatinine_phosphokinase':creatinine_phosphokinase,
+            'diabetes':diabetes,'ejection_fraction':ejection_fraction,
+            'high_blood_pressure':high_blood_pressure,'platelets':platelets,
+            'serum_creatinine':serum_creatinine,'serum_sodium':serum_sodium,
+            'sex':sex,'smoking':smoking,'time':time}
+    # 
+    features = pd.DataFrame(data,index=[0])
+    return features
+
+input_df = user_input_features()
+
+st.subheader('Medical Data📋')
+st.write(input_df)
+
+with open('death_pred.pkl', 'rb') as file:
+    load_model = pickle.load(file)
+
+# Access the individual models
+model_xgboost = load_model['model_xgboost']
+
+if st.button('Predict'):
+    try:
+        prediction = model_xgboost.predict(input_df)
+        prediction_proba = model_xgboost.predict_proba(input_df)
+
+        st.subheader('DIAGNOSIS')
+        for i in range(len(prediction)):
+            if prediction[i] == 0:
+                st.success("The patient is in good condition")
+            else:
+                st.error("This Patient is at **Risk of Death**")
+
+        st.subheader('Probability')
+        st.write("0 : No | 1 : Yes")
+        st.write(prediction_proba)
+
+    except ValueError:
+        st.header("Insufficient Medical Data")

death_pred.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a05a5a9fd48de6623ffed2e74e37344553a7086322b944de10095394828a136e
+size 201162

requirements.txt

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+streamlit
+pandas
+scikit-learn==1.1.1
+feature_engine==1.5.2