A hybrid machine learning model using feature selection on the UCI Heart Disease Dataset achieved 84.24% accuracy in discriminating patients with heart disease from healthy individuals.
Heart diseases are the undisputed leading causes of death globally. Unfortunately, the conventional approach of relying solely on the patient’s medical history is not enough to reliably diagnose heart issues. Several potentially indicative factors exist, such as abnormal pulse rate, high blood pressure, diabetes, high cholesterol, etc. Manually analyzing these health signals’ interactions is challenging and requires years of medical training and experience. Therefore, this work aims to harness machine learning techniques that have proved helpful for data-driven applications in the rise of the artificial intelligence era. More specifically, this paper builds a hybrid model as a tool for data mining algorithms like feature selection. The goal is to determine the most critical factors that play a role in discriminating patients with heart illnesses from healthy individuals. The contribution in this field is to provide the patients with accurate and timely tentative results to help prevent further complications and heart attacks using minimum information. The developed model achieves 84.24% accuracy, 89.22% Recall, and 83.49% Precision using only a subset of the features.
Alfadli et al. (Thu,) studied this question.