Use of Machine Learning Techniques for Predicting Heart Disease Risk from Phone Enquiries Data
Martin-Rodriguez, F.; Pajaro-Lorenzo, J.; Isasi-de-Vicente, F.; Fernandez Barciela, M.
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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.
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