Heart Disease Prediction using Clustered Genetic Optimization Algorithm

One of the key topics of research in the Health Care Management system (HMS) is the early forecast of heart disease. Only by foreseeing cardiac illnesses in advance can we minimize the number of fatalities. Machine learning (ML) approaches are used to predict the development of cardiac illnesses, but they have not yet produced results that are adequate since they heavily rely on the data that is now available. For the prediction of cardiac illness, we suggested a brand-new Clustered Genetic Algorithm approach (C-GA). Using segmentation techniques, we grouped the data in this process, and then we used the genetic optimization approach for each clustered data set. For experimentation, we take into account the benchmark data set which is the UCI heart disease data set. The results of the experiment demonstrate that the provided model produced a superior forecast.