Early-Stage Detection Of Cardiac Heart Disease for Pregnancy Women

Abstract

Introduction: CVDs are one of the main factors associated with MM and MMR, including pregnancy, where Women’s cardiovascular systems are further compromised by physiological changes during pregnancy. This paper proposes detailed machine learning algorithms for detecting cardiac heart disease in pregnant women by applying existing clinical parameters and non- invasive tests. The results of flow-diagram and three classifiers, namely Logistic Regression, Support Vector Machine, Random Forest, were examined in terms of their accuracy, precision, recall and F1-score. Of all the models Random Forest delivered the highest recall, 95%, the accuracy of 92.5% making them appropriate for clinical decision support systems (CDSS) in early stage Cardiac disease diagnosis.