Prediction of Heart Diseases Using Hayes Process Macro Serial Mediation Model 6

Heart diseases are among one of the most common reasons of fatalities in the modern world due to a variety of life style and environmental factors. This study deals with the prediction of heart diseases in a population based on the relation between age and the maximum heart rate of a set of individuals. This relationship is also assessed with the introduction of two mediators namely resting blood pressure and cholesterol. The dataset for the study is the Cleveland dataset which is used by machine learning researchers. The data was subject to serial mediation Model 6 test based on Andrew Hayes PROCESS data analysis technique. The model test results have beenpresented and interpreted in light of the model test results.