基于人工神经网络的心脏病诊断决策支持系统

S. Ghwanmeh, A. Mohammad, A. Al-Ibrahim
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引用次数: 64

摘要

并非每个医疗中心都能进行心脏诊断,特别是在农村地区,由于缺乏先进的心脏诊断设备,那里的支持和护理较少。此外,医生的直觉和经验并不总是足以达到高质量的医疗程序结果。因此,医疗错误和不良结果是需要非常规计算机诊断系统的原因,这反过来又减少了医疗致命错误,增加了患者的安全并挽救了生命。该解决方案基于人工神经网络(ann),提供了一个决策支持系统来识别三种主要的心脏病:二尖瓣狭窄、主动脉狭窄和室间隔缺损。此外,该系统为开发一种可操作的心脏病诊断筛查和测试设备提供了一个令人鼓舞的机会,可以为临床医生提供很大的帮助,以进行高级心脏病诊断。利用真实的医疗数据,进行了一系列的实验来检验所提出的解决方案的性能和准确性。对比结果表明,系统的性能和准确率都是可以接受的,对心脏病的分类准确率达到92%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis
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%.
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