Heart Disease Prediction

Heart Disease Prediction 

Prediction Technical Specifications:

     Data:

           The data is from the UCI Machine Learning Repository Heart Disease Data Set.

           https://archive.ics.uci.edu/ml/datasets/Heart+Disease

           Contains 303 samples with 13 input feauts, and binary target values

     Input Features (as seen in GUI form):

           Age - real number

           Sex - categorical

           Chest pain type - categorical

           Resting blood pressure - real number

           Serum cholesterol - real number

           Fasting blood sugar - real number (internally converted to categorical)

           Resting ECG results - categorical

           Maximum achievable heart rate - real number

           Exercised induced angina - categorical

           ST depression induced by exercise relative to rest - real number

           Slope of the peak exercise ST segment - categorical

           Number of major vessels colored by floroscopy - real number

           Thallium heart scan - categorical

     Feature encoding:

           All categorical features are converted to binary using a

           one-hot encoder

     Feature normalization:

           All real numbers are scaled using a standard scaler (subtract mean

           and divide by standard deviation)

     Machine Learning Algorithm:

           A radial basis kernel SVM classifier is used for making predictions.

           The output is a probability representing the likelihood of the

           presence of heart disease.

     Expected Accuray:

           The data was tested using a 10-fold cross validation technique.

           The results are:

           Avg Accuracy   Avg Recall     Avg Precision  Avg ROC_AUC

           0.842          0.792          0.863          0.932

Author : ss infotech

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