Use of Machine Learning Techniques for Predicting Heart Disease Risk from Phone Enquiries Data

This paper is about the application of known machine learning (ML) techniques for the prediction of heart disease risk. A public database is used to train and test the ML models. Results are evaluated using standard measures like precision, recall and F-score. ML models selected are well known techniques and they are based on different approaches. Chosen methods are: MLP (Multi-Layer Perceptron), SVM (Support Vector Machine) and Bagged Tree (Bootstrap Aggregated Trees). After evaluating techniques alone on their own, a new “triple voting method” (TVM) is tested applying the three individual methods and “adding” their results to improve accuracy.