基于特征选择的神经网络心脏病预测敏捷方法

Ankit Maithani, Reetika Koli, Ritu Pal, Dhajvir Singh Rai, A. Rohilla, Rahul Kumar
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引用次数: 0

摘要

最近的研究表明,全球近30%的死亡是由心脏病引起的。医学诊断主要是由专家的技能和经验完成的,但有时会报告错误的诊断,因此医生建议患者进行各种测试以进一步分析,这是非常昂贵和耗时的,因为医学数据库庞大,无法快速处理。本文利用带特征选择的神经网络预测患者患心脏病的可能性。将该方法应用于数据集,通过减少现有数据集中不必要的属性来提高性能,从而获得更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Agile Approach of Predicting Cardiac Disease using ANN Based on Feature Selection
Recent study shows that almost 30% of total global deaths are caused by heart disease. Medical diagnosis is done mainly by specialist's skill and experience but sometime cases are reported of wrong diagnosis therefore the doctor advises patients to take various tests for further analysis which is very expensive and time consuming as medical databases are huge and cannot be processed quickly. In this paper we have predicted heart disease possibility in patients with the help of neural network with Feature selection. This approach was applied to the dataset to get the better results and to increase the performance by reducing the unnecessary attributes from the existing dataset.
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