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  • 标题:A comprehensive study on disease risk predictions in machine learning
  • 本地全文:下载
  • 作者:G. Saranya ; A. Pravin
  • 期刊名称:International Journal of Electrical and Computer Engineering
  • 电子版ISSN:2088-8708
  • 出版年度:2020
  • 卷号:10
  • 期号:4
  • 页码:4217-4225
  • DOI:10.11591/ijece.v10i4.pp4217-4225
  • 出版社:Institute of Advanced Engineering and Science (IAES)
  • 摘要:Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. Comprehensive survey on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavours have been shifted.
  • 关键词:Machine learning;Disease prediction;Heart disease;Breast cancer;Predictive models
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