DefinePK

Abstract

Across the world, 17 million people die from heart disease each year. Heart-related diseases were the main cause for about 19% percent of deaths in Pakistan in 2016, the same has since now risen to 29%. As per most recent WHO statistics regarding prevalence of heart attacks in Pakistan, approximately more than two hundred thousand (200000) persons died in Pakistan in 2020 due to coronary heart disease, making up 16.49 percent of all fatalities. With a death rate of 193.56 per 100,000 inhabitants, Pakistan is ranked at number 30 in the world. Rising death rate due to heart disease can be minimized through detection at early stage. Different data mining approaches have made early detection of cardiac disease possible. Certain datasets are being used to retrieve useful information. Several machine learning techniques / models have been proved to be the most effective, accurate and profitable to detect cardiovascular disease at an early stage. However, the approach of machine learning and Genetic Algorithm (GA) with feature selection may aid in lowering the computational complexity of GA and increasing the effectiveness of its search for ideal solutions. Hence, there is dire need to apply such hybrid approach to get much more effective and accurate results. The goal of this survey paper is to review different papers related to CVD prediction at early stage by applying hybrid approach of ML Techniques with Genetic Algorithm. Moreover, the results obtained by the authors in reviewed papers are also examined. In the end, this survey will showcase the importance of the hybrid approach in improving accuracy of ML results.