Customized Prediction of Cardiovascular Disease Using Machine Learning: Analysis of Regional Risk Factors
Keywords:
CVD, Prediction of CVD, Machine learning algorithms, CVD IndexAbstract
Cardio Vascular Disease (CVD) is the most silent killer the world has ever witnessed. The silence of the disease is the salient feature and the most deadly of all the characteristics of the disease. It is a combination of various collections of ailments of the blood vessels and heart. CVD cannot be considered as a single disease and it adds more complexity to diagnosis and prediction at an early stage. The paper uses standard data available in the public domain. Analysis and review of literature indicates not set of parameters are enough for prediction of CVD and these parameters vary from region to region. The paper provides a novel method which uses a combination of machine learning algorithms to calculate the probability of risk of individual using known parameters. The paper provides an algorithm for establishing set of best fit parameters, set of best fit Machine learning methods and also provides analysis of prediction using inbuilt tool box of Python vs the novel CVD index method. The paper considers customization of data and prediction methods for each region which provides better results.
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