Prediction of heart disease using neural network

Tülay Karayılan, Özkan Kiliç
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引用次数: 87

Abstract

Heart disease is a deadly disease that large population of people around the world suffers from. When considering death rates and large number of people who suffers from heart disease, it is revealed how important early diagnosis of heart disease. Traditional way of diagnosis is not sufficient for such an illness. Developing a medical diagnosis system based on machine learning for prediction of heart disease provides more accurate diagnosis than traditional way. In this paper, a heart disease prediction system which uses artificial neural network backpropagation algorithm is proposed. 13 clinical features were used as input for the neural network and then the neural network was trained with backpropagation algorithm to predict absence or presence of heart disease with accuracy of 95%.
用神经网络预测心脏病
心脏病是一种致命的疾病,全世界有很多人都患有这种疾病。当考虑到死亡率和心脏病患者人数众多时,揭示了心脏病早期诊断的重要性。对于这种疾病,传统的诊断方法是不够的。开发一种基于机器学习的医疗诊断系统来预测心脏病,可以提供比传统方法更准确的诊断。本文提出了一种基于人工神经网络反向传播算法的心脏病预测系统。将13个临床特征作为神经网络的输入,然后用反向传播算法对神经网络进行训练,预测有无心脏病,准确率达到95%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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