On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders

This research aims to enhance the classification and prediction of ischemic heart diseases using machine learning techniques, with a focus on resource efficiency clinical applicability. Specifically, we introduce novel non-invasive indicators known as Campello de Souza features, which require only tensiometer clock for data collection. These features were evaluated comprehensive dataset disease cases from repository. Our findings highlight ability algorithms not streamline diagnostic procedures but also reduce errors dependency extensive testing. Three key features—mean arterial pressure, pulsatile blood pressure index, resistance-compliance indicator—were found significantly improve accuracy in binary classification. Logistic regression achieved highest average among examined classifiers when utilizing these features. While such contribute substantially process, they should be integrated into broader framework that includes patient evaluations medical expertise. Therefore, present study offers valuable insights leveraging science techniques diagnosis management cardiovascular diseases.