使用机器学习的疾病预测分析技术

R. Naaz, V. Karunakaran
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引用次数: 0

摘要

由于医疗保健领域对基于PC的创新的无限接受,电子数据已被收集起来。本文的主要目的是讨论数据挖掘在提供医疗保健方面的应用。这些数据挖掘方法也可以应用于许多考试和教育环境中。临床科学中发展最快的领域是智能健康预测系统。计算机科学的一个领域被称为数据挖掘,它利用临床领域已有的信息来估计疾病的发生。我们可以通过使用数据库管理和机器学习工具提取新模式,从大型数据集组中学习。在这篇研究论文中,我们研究了已经应用于疾病预测的不同算法数据挖掘策略。在生物信息学研究中,卫生组织经常使用数据挖掘对糖尿病和癌症等疾病进行分类。关于如何将数据挖掘方法与机器学习结合起来,根据客户端副作用预测疾病的回顾在随附的论文中进行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive Analysis Techniques of Disease using Machine Learning
Electronic data have collected because of the health care area's boundless reception of PC based innovation. The primary goal of this essay is to discuss data mining's application to the provision of medical care. These data mining methods can also be applied in a number of examination and educational settings. The quickest arising area of clinical science is the Smart Health Prediction System. One area of computer science called data mining uses the information already available in the clinical field to estimate the event of diseases. We can learn from groups of large datasets by extracting new patterns using database management and machine learning tools. In this research paper, we examine different algorithmic data mining strategies that have been applied to the prediction of illness. Health organizations frequently use data mining to categorize diseases like diabetes and cancer in bioinformatics research. The review on how data mining methods are joined with machine learning to anticipate diseases in light of client side effects is made in the accompanying paper.
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