机器学习技术、大数据分析在医疗保健领域的应用——文献综述

Q3 Medicine
M. Sughasiny, J. Rajeshwari
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引用次数: 11

摘要

数据挖掘在贸易、商业和电子商务等非常明显的领域的成功应用,已经指向了它在另一个行业的应用。医疗条件仍然知识丰富,但信息匮乏。在医疗实践中有大量可行的信息。然而,仍然缺乏必要的调查机制来识别数据中隐藏的趋势和关系。许多研究者已经将数据挖掘方法应用于多种疾病的预后和诊断。机器学习方法已广泛应用于不同疾病的早期预测。近十年来,与开发研究、患者自我跟踪和健康记录相关的电子数据在种类和数量上都出现了异常发展,这些数据被统称为大数据。本文对特征选择方法、有监督机器学习方法、无监督机器学习方法和大数据对医疗保健行业的重要性进行了全面的文献综述。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application Of Machine Learning Techniques, Big Data Analytics In Health Care Sector – A Literature Survey
The triumphant utilization of data mining in extremely evident areas like trade, commerce, and e-business has directed to its application in another industry. The medical conditions are still knowledge rich but information low. There is an abundance of information feasible inside the medical practices. Still, there is a shortage of essential investigation mechanisms to recognize hidden trends and relationships in data. Many researchers have applied Data Mining methods for the prognosis and diagnosis of several diseases. Machine Learning methods have broadly utilized in the prognostication of different diseases at the beginning stages. The current decade has observed an abnormal development in the variety and volume of electronic data associated with the development and research, patient self-tracking, and health records together suggested to as Big Data. This paper presents a comprehensive literature survey on the importance of Feature Selection methods, Supervised Machine Learning methods, Unsupervised Machine Learning methods and big data for the healthcare industry.
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来源期刊
Koomesh
Koomesh Medicine-Medicine (all)
CiteScore
0.80
自引率
0.00%
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0
审稿时长
24 weeks
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