Heart Disease Prediction App

.streamlit/config.toml

@@ -0,0 +1,6 @@
+[theme]
+primaryColor="#666666"
+backgroundColor="#EEFFFF"
+secondaryBackgroundColor="#FFFFFF"
+textColor="#262730"
+font="sans serif"

README.md

@@ -1,6 +1 @@
-# Welcome to Streamlit!
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).

config.toml

@@ -0,0 +1,6 @@
+[theme]
+primaryColor="#666666"
+backgroundColor="#EEFFFF"
+secondaryBackgroundColor="#FFFFFF"
+textColor="#262730"
+font="sans serif"

requirements.txt

@@ -1,3 +1,7 @@
 altair
 pandas
 streamlit
+scikit-learn
+prediction
+colour
+

streamlit_app.py

@@ -3,38 +3,179 @@
 import pandas as pd
 import streamlit as st
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+
+st.title("Welcome to Heart Disease Risk Prediction!!")
+
+st.header("Fill out the Following Questions:")
+
+st.write("Personal Detail Question")
+username = st.text_input("Enter your name")
+sex = st.radio("Select Gender:", ("Male", "Female"))
+age = st.selectbox("Select Age Group:", 
+                   ["18-24", "25-29", "30-34", "35-39", "40-44", "45-49", "50-54",
+                    "55-59", "60-64", "65-69", "70-74", "75-79", "80 or older"],
+                    placeholder="Select One")
+race = st.selectbox("Select Race:", ["White", "Hispanic", "Black", "Asian", "American Indian/Alaskan Native"],
+                    placeholder="Select One")
+height = st.number_input("Enter your Height (in centimeters):", min_value=100.00, max_value=250.00, 
+                         value=150.00, step=5.00)
+weight = st.number_input("Enter your Weight (in kilograms):", min_value=30.00, max_value=200.00, 
+                         value=50.00, step=5.00)
+
+
+st.text(" ")
+st.text(" ")
+st.write("Daily Activity Question")
+sleep = st.number_input("Enter Average Sleep Time:", min_value=0.0, max_value=24.0, 
+                        value=7.0, step=0.5)
+activity = st.radio("During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?:", 
+                    ("Yes", "No"), index=1)
+smoking = st.radio("Have you smoked at least 100 cigarettes in your entire life?:", 
+                   ("Yes", "No"), index=1)
+drinking = st.radio("Do you drink a lot (adult men having more than 14 drinks per week or adult women having more than 7 drinks per week)?:", 
+                    ("Yes", "No"), index=1)
+
+
+st.text(" ")
+st.text(" ")
+st.write("Health Status")
+genhealth = st.selectbox("Would you say that in general your health is:", ['Excellent', 'Very good', 'Good', 'Fair', 'Poor'],
+                         index=2)
+physicalhealth = st.selectbox("During the past month, how many days did you have physical health problem, which includes physical illness and injury?:", range(0, 31))
+mentalhealth = st.selectbox("During the past month, how many days did you have mental health problem?:", range(0, 31))
+diffwalking = st.radio("Do you have serious difficulty walking or climbing stairs?:", 
+                       ("Yes", "No"), index=1)
+asthma = st.radio("(Ever told) (you had) asthma?:", ("Yes", "No"), index=1)
+
+st.text(" ")
+st.text(" ")
+button = st.button('Predict')
+
+bmi = weight / ((height/100)**2)
+
+
+
+
+# Creating a Machine Learning Model 
+
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.feature_selection import RFE
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+
+over_sampled_df = pd.read_csv("./over_sampled_df_fe.csv")
+test_sampled_df = pd.read_csv("./test_sampled_df_fe.csv")
+
+X_train_over = over_sampled_df.drop(columns="HeartDisease")
+y_train_over = over_sampled_df["HeartDisease"]
+
+X_test_sampled = test_sampled_df.drop(columns="HeartDisease")
+y_test_sampled = test_sampled_df["HeartDisease"]
+
+scaler = StandardScaler()
+scaler.fit(X_train_over)
+X_scaled_train = scaler.transform(X_train_over)
+X_scaled_test = scaler.transform(X_test_sampled)
+
+rfe_over = RFE(estimator=LogisticRegression(max_iter=1000, random_state=42), n_features_to_select=11)
+rfe_over.fit(X_scaled_train,y_train_over)
+import joblib
+joblib.dump(rfe_over, "cap_rfe.sav")
+
+scaler = StandardScaler()
+scaler.fit(X_train_over.loc[:, rfe_over.support_])
+X_scaled_train = scaler.transform(X_train_over.loc[:, rfe_over.support_])
+X_scaled_test = scaler.transform(X_test_sampled.loc[:, rfe_over.support_])
+import joblib
+joblib.dump(scaler, "cap_scaler.sav")
+
+my_PCA = PCA()
+my_PCA.fit(X_scaled_train)
+X_train_PCA = my_PCA.transform(X_scaled_train)
+X_test_PCA = my_PCA.transform(X_scaled_test)
+import joblib
+joblib.dump(my_PCA, "cap_pca.sav")
+
+cap_model = LogisticRegression(max_iter=1000, C=0.0001, penalty="l2", solver="liblinear", random_state=42)
+cap_model.fit(X_train_PCA,y_train_over)
+import joblib
+joblib.dump(cap_model, "cap_model.sav")
+
+
+#from prediction import predict
+def predict(df):
+    scaler = joblib.load("cap_scaler.sav")
+    rfe_over = joblib.load("cap_rfe.sav")
+    my_PCA = joblib.load("cap_pca.sav")
+    cap_model = joblib.load("cap_model.sav")
+
+    yes_no_columns = ["Smoking", "AlcoholDrinking", "DiffWalking", "PhysicalActivity", "Asthma"]
+
+    for column in yes_no_columns:
+        df[column] = df[column].map({"No":0, "Yes":1})
+
+    df["Sex"] = df["Sex"].replace({"Male":1, "Female":0})
+
+    df["AgeCategory"] = df["AgeCategory"].replace({'55-59':55, '80 or older':80, '65-69':65, '75-79':75,
+                                                        '40-44':40, '70-74':70,'60-64':60, '50-54':50, '45-49':45,
+                                                        '18-24':18, '35-39':35, '30-34':30, '25-29':25})
+
+    df["GenHealth"] = df["GenHealth"].replace({'Very good':3, 'Fair':1, 'Good':2, 'Poor':0, 'Excellent':4})
+
+
+    race_column = ["Race_American Indian/Alaskan Native", "Race_Asian", "Race_Black", "Race_Hispanic", "Race_Other", "Race_White"]
+    df[race_column] = 0
+    race_list = ["American Indian/Alaskan Native", "Asian", "Black", "Hispanic", "Other", "White"]
+    for index in range(len(race_list)):
+        if df.loc[0, "Race"] == race_list[index]:
+            df[race_column[index]] = 1
+    cap_df = df.drop(columns="Race")
+
+
+    scaled_df = scaler.transform(cap_df.loc[:, rfe_over.support_])
+
+    pca_df = my_PCA.transform(scaled_df)
+
+    cap_proba = cap_model.predict_proba(pca_df)
+    cap_pred = (cap_proba[:, 1] >= 0.52).astype(int)
+
+    #import joblib
+    joblib.dump(cap_proba, "cap_proba.sav")
+
+    if cap_pred == 1:
+        #result_high='<p style="color:Red; font-size: 25px;">HIGH</p>'
+        st.markdown(f"Based from the Machine Learning model,")
+        st.markdown(f"your risk of developing Heart Disease is:")
+        #st.markdown(result_high, unsafe_allow_html=True)
+
+        st.error('HIGH')
+
+
+    else:
+        st.balloons()
+        #result_low='<p style="color:Blue; font-size: 25px;">LOW</p>'
+        st.markdown(f"Based from the Machine Learning model, your risk of developing Heart Disease is:")
+        #st.markdown(result_low, unsafe_allow_html=True)
+
+        st.warning('LOW')
+
+
+data = [bmi, smoking, drinking, physicalhealth, mentalhealth, diffwalking, sex, age, race, activity, genhealth, sleep, asthma]
+user_df = pd.DataFrame(data, index = ["BMI", "Smoking", "AlcoholDrinking", "PhysicalHealth", "MentalHealth", "DiffWalking", "Sex", "AgeCategory", "Race", "PhysicalActivity", "GenHealth", "SleepTime", "Asthma"])
+user_df = user_df.T
+
+
+if button:
+    st.text(f"Hi {username}!")
+    predict(user_df)
+
+    st.text(" ")
+    st.text(" ")
+    cap_proba = joblib.load("cap_proba.sav")
+    st.markdown(f"According to the machine learning model:")
+    st.success(f"The Probability that it will classify you as at risk for heart disease are:   {int(cap_proba[:, 1]*100)}%, the Probability that it will classify you as at no risk for heart disease are:   {int(cap_proba[:, 0]*100)}%")
+    st.text(" ")
+    st.text(" ")
+    st.markdown(f"Note: If the Probability it will classify you at risk is over 52%, then the model will classify you as at risk for heart disease")