Using a genetic machine learning method to detect cardiac arrest

Abstract

The leading cause of death worldwide is cardiac arrest. The diagnosis of heart failure is a tedious task. An intelligent illness detection system is a bare minimum need for disease prediction. Data mining techniques are also applied to determine a patient's health or sickness. Data mining techniques have been widely employed in intelligent medical systems to accurately estimate and identify various illnesses. These techniques were especially helpful in creating systems for health assistance because they may reveal hidden correlations and trends in patient information. The identification of heart disease, one of the leading causes of mortality worldwide, is one of the most important uses of such instruments. Nearly all programmes that predict heart diseases employ clinical data together with inputs and parameters.