A Hybrid Random Forest Linear Model approach to predict the Heart Disease

Abstract

The project focuses mainly on cardiovascular disease prediction in the real world. Heart disease prediction involves various risk factors. AI (ML) is showing that it contributes to making decisions and based on the huge amount of information provided by the medical services industry. A brief look at predicting heart infection with ML procedures is provided by various exams. We propose a new strategy in this article that aims to identify significant characteristics by applying AI techniques to improve the cardiovascular expectation accuracy. The preliminary model offers a range of highlights and a number of known methods of classification. By the coronary disease expectation model with a hybrid random forest linear model that is blend of two different algorithms. We produce an improved presentation level with an exactness level of 88.7 percent. Dataset is from the UCI repository. The proposed HRFLM algorithm will assist doctors in diagnosis heart patients.