Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis

TLDR

Heart disease diagnosis is limited in rural centers due to scarce equipment and reliance on physician intuition, creating medical errors that necessitate computer‑based systems to improve safety and save lives. The authors propose an ANN‑based decision support system that identifies mitral stenosis, aortic stenosis, and ventricular septal defect, offering a screening tool to aid clinicians in advanced heart diagnosis. The system was trained and evaluated on real medical data through a series of experiments assessing its performance and accuracy. The model achieved a 92 % classification accuracy for the three heart diseases.

Abstract

Heart diagnosis is not always possible at every medical center, especially in the rural areas where less support and care, due to lack of advanced heart diagnosis equipment. Also, physician intuition and experience are not always sufficient to achieve high quality medical procedures results. Therefore, medical errors and undesirable results are reasons for a need for unconventional computer-based diagnosis systems, which in turns reduce medical fatal errors, increasing the patient safety and save lives. The proposed solution, which is based on an Artificial Neural Networks (ANNs), provides a decision support system to identify three main heart diseases: mitral stenosis, aortic stenosis and ventricular septal defect. Furthermore, the system deals with an encouraging opportunity to develop an operational screening and testing device for heart disease diagnosis and can deliver great assistance for clinicians to make advanced heart diagnosis. Using real medical data, series of experiments have been conducted to examine the performance and accuracy of the proposed solution. Compared results revealed that the system performance and accuracy are acceptable, with a heart diseases classification accuracy of 92%.