Enhanced Detection of Chronic Heart Disease: A Novel Approach With Extreme Gradient Boosting-Logistic Regression Algorithm

Abstract

In recent years, biomedical healthcare applications for managing and treating chronic heart diseases have increased. Chronic heart disease is a serious and sometimes deadly ailment that affects millions of individuals globally. Therefore, timely and effective detection of heart disease is crucial in medicine, particularly in cardiology. These complications can lead to reduced quality of life, increased medical costs, and increased risk of death. Obesity, smoking, and high blood pressure are some of the risk factors that worsen chronic heart disease. However, challenges occur in accurately and sensitively classifying heart disease in performance evaluation. To resolve this problem, we propose an Extreme Gradient Boosting-Logistic Regression (EGBLR) algorithm to accurately and quickly predict chronic heart disease. Furthermore, pre-processing can be performed using the Normalized Minimum-Maximum Scaling Feature (NMMSF) technique to estimate missing or duplicate values. Next, the Inter-Quartile Range Optimization (IQRO) technique can analyze heart disease data to identify and remove outliers. After that, the Relief Feature Vector (RFV) algorithm can be used to find each feature weight. Finally, the proposed EGBLR method can be employed to detect normal or abnormal chronic heart diseases and increase accuracy in diagnosis. The evaluation of the proposed techniques can be predicted using performance matrices such as recall, sensitivity score, precision, false rate, and precision. The accuracy of the proposed EGBLR detection approach exceeds that of previous techniques. Moreover, the proposed method for diagnosing chronic heart disease can be easily adapted to biomedical healthcare applications.