The figure below shows the predicted probability a patient with the given characteristics will be AT RISK or NOT AT RISK.
Based on the provided threshold, a statement below the figure will classify the patient as AT RISK or NOT AT RISK of Heart Disease.
The figure below shows the ROC curve for the logistic regression model used for predictions. Numbers below shows the performance metrics for the model.
Adjust the threshold through the slide-bar on the side panel on the left to see the impact to accuracy, sensitivity, and specificity.
Adjust Threshold slider input on the left Sidebar to obtain desired accuracy, sensitivity, and specificity
This R Shiny web app allows the user to perform heart disease prediction based on certain characteristics of heart disease. The app is developed based on logistic regression algorithm and using UCI machine learning data.
I would like to continue enhancing this app with many additional features and graphics. Stay tuned for updates.