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Update HeartDisease.py #1

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26b4f8a
Update HeartDisease.py
kevJ711 Nov 24, 2023
af42896
Merge branch 'HeartDisease.py' into kevJ711-patch-1
kevJ711 Dec 17, 2023
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25 changes: 8 additions & 17 deletions HeartDisease.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,5 @@
import pandas as pd
from sklearn.metrics import accuracy_score, classification_report
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from PIL import Image
Expand Down Expand Up @@ -111,8 +110,6 @@ def get_user_input():
st.write("Classification Report:")
st.write(classification_report(Y_test, naive_bayes_classifier.predict(X_test)))



# Evaluate XGBooster Classifier

st.subheader("XGBooster Classifier:")
Expand Down Expand Up @@ -158,7 +155,7 @@ def get_user_input():
# Adding the XGBoost ROC curve
fig.add_trace(go.Scatter(x=xgboost_fpr, y=xgboost_tpr, mode='lines', name=f'XGBoost (AUC={xgboost_auc:.2f})'))

# Seting the the layout
# Setting the layout
fig.update_layout(
title='Receiver Operating Characteristic (ROC) Curve',
xaxis=dict(title='False Positive Rate'),
Expand All @@ -178,22 +175,16 @@ def get_user_input():
# Adding the XGBoost ROC curve
fig.add_trace(go.Scatter(x=xgboost_fpr, y=xgboost_tpr, mode='lines', name=f'XGBoost (AUC={xgboost_auc:.2f})'))

# Seting the the layout
fig.update_layout(
title='Receiver Operating Characteristic (ROC) Curve',
xaxis=dict(title='False Positive Rate'),
yaxis=dict(title='True Positive Rate'),
legend=dict(x=0.7, y=0.2),
)

# Display the results of the random forest model
print('Result for Random Forest\n0 = Positive, 1 = Negative')
print(f"Accuracy for Random Forest Classifier: {rf_accuracy * 100:.2f}%")
st.subheader('Result for Random Forest\n0 = Positive, 1 = Negative')
st.subheader(f"Accuracy for Random Forest Classifier: {rf_accuracy * 100:.2f}%")

if int(rf_accuracy) == 0:
print("By using the rf model We suspect you may be dealing with a Heart Disease.")
print("It is advisable to consult with a healthcare professional to make sure.")
st.subheader("By using the rf model We suspect you may be dealing with a Heart Disease.")
st.subheader("It is advisable to consult with a healthcare professional to make sure.")
else:
print("By using the rf model We suspect that you are not dealing with a Heart Disease")
st.subheader("By using the rf model We suspect that you are not dealing with a Heart Disease")

# Displaying the predicted result for Naive Bayes
st.subheader('\nResult for Naive Bayes\n0 = Positive, 1 = Negative')
Expand Down