Recursion Enhanced Random Forest With an Improved Linear Model (RERF-ILM) for Heart Disease Detection on the Internet of Medical Things Platform

Abstract

Nowadays, Heart disease is one of the crucial impacts of mortality in the country. In clinical data analysis, predicting cardiovascular disease is a primary challenge. Deep learning (DL) has been demonstrated to be effective in helping to determine and forecast a huge amount of data produced by the health industry. In this paper, the proposed Recursion enhanced random forest with an improved linear model (RFRF-ILM) to detect heart disease. This paper aims to find the key features of the prediction of cardiovascular diseases through the use of machine learning techniques. The prediction model is adding various combinations of features and various established methods of classification. it produces a better level of performance with precision through the heart disease prediction model. In this study, the factors leading to cardiovascular disease can be diagnosed. A comparison of important variables showed with the Internet of Medical Things (IoMT) platform, for data analysis. This indicates that coronary artery disease develops more often in older ages. Also important in this disease's outbreak is high blood pressure. For this purpose, measures must be taken to prevent this disease and Diabetes provides a further aspect that should be taken into consideration in the occurrence of coronary artery disease with 96.6 % accuracy,96.8% stability ratio and 96.7% F-measure ratio.

Keywords

• Heart disease detection
• linear model
• random forest
• machine learning
• diagnosis