Logical Mining Assisted Heart Disease Prediction Scheme in Association with Deep Learning Principles

Abstract

An estimated 350 million young adults (between the ages of 30 and 40) would have heart disease by 2030, according to the WHO. These individuals will be at risk for renal problems, stroke, as well as peripheral vascular disease. Heart disease is the leading cause of death in the modern era. Most individuals cannot afford the high expense of heart disease therapy. Because of this, a Heart Disease Prediction Scheme can help alleviate this issue. It aids in the earlier detection of cardiovascular disease. For the development of the Heart Disease Prediction Scheme, data mining methods are employed. A variety of healthcare data formats, including pictures, text, charts, and figures, are used in various systems. To diagnose cardiac disease early, we examine risk factors including system conditions. the selection of risk predictors, the use of efficient methods for identifying and extract key information to describe aspects of developing a prediction model We can quickly diagnose heart illness with multiple features and risk factor specifications using the new technique called Intelligent Learning Assisted Support Vector [ILASV]. Mining concepts are used to identify high-risk variables for heart disease based on these criteria. Fast and accurate illness predictions will be made possible by the application of data mining methods.