S. Mehrabi, M. Maghsoudloo, H. Arabalibeik, R. Noormand and Y. Nozari, “Application of Multilayer Perceptron and Radial Basis Function Neural Networks in Differentiating between Chronic Obstructive Pulmonary and Congestive Heart Failure Diseases,” Expert Systems with Applications, Vol. 36, No. 3, 2009, pp. 6956-6959. doi:10.1016/j.eswa.2008.08.039

has been cited by the following article:

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

  AUTHORS: Sameh Ghwanmeh, Adel Mohammad, Ali Al-Ibrahim

  KEYWORDS: Heart Disease Diagnosis; Classification Accuracy; ANNs; Decision Support System; Knowledge Base

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.5 No.3, July 31, 2013

  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%.