Statistics and Deep Belief Network-Based Cardiovascular Risk Prediction

IntroductionCardiovascular diseases include hyperlipidemia, myocardial infarction, and angina pectoris.Cardiovascular disease is diagnosed by electrocardiography, ultrasound, blood tests, angiography, and so on.These methods are time-consuming and costly because they require many different tests.Recently, a cardiovascular disease prediction technique using machine learning has been developed to replace these diagnostic methods [1].Medical IT combined with machine learning technology has increased the accuracy of disease prediction using predictive models generated from disease-related learning data [2].However, since complex data is analyzed, a deep learning technique is required [3,4].Many studies have been conducted on cardiovascular disease using machine learning.Khatib and Montazer [5] developed a heart disease risk prediction model based on the Dempster-Shafer evidence theory by designing a fuzzyevidential hybrid inference engine.Krishnaiah et al. [6] developed a cardiovascular risk prediction system using fuzzy K-nearest neighbor (K-NN) classifiers for measured values to remove uncertainty.However, research on a prediction model for domestic cardiovascular disease is lacking [7,8].In recent years, attention has focused on how to construct a prediction model based on big data and the development of deep learning technology.Prediction models are based on artificial intelligence (AI), and many methods using machine learning, data mining,