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A Empirical study on Disease Diagnosis using Data Mining Techniques

机译:利用数据挖掘技术进行疾病诊断的实证研究

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摘要

Data mining is an essential part in learning disclosure process where intelligent agents are incorporated for pattern extraction. In the process of developing data mining applications the most challenging and interesting task is the disease prediction. This paper will be helpful for diagnosing accurate disease by medical practitioners and analysts, portraying various data mining techniques. Data mining applications in medicinal services holds colossal potential and convenience. However the efficiency of data mining techniques on healthcare domain depends on the availability of refined healthcare data. In our current study we discuss few classifier techniques used in medical data analysis. Also few disease prediction analysis like breast cancer prediction, heart disease diagnosis, thyroid prediction and diabetic are considered. The result shows that Decision Tree algorithm suits well for disease prediction as it produces better accuracy results.
机译:数据挖掘是学习披露过程中不可或缺的部分,在学习披露过程中,将智能代理合并到模式提取中。在开发数据挖掘应用程序的过程中,最具挑战性和最有趣的任务是疾病预测。本文将对描绘各种数据挖掘技术的医疗从业人员和分析人员进行准确的疾病诊断提供帮助。医药服务中的数据挖掘应用具有巨大的潜力和便利性。但是,医疗领域的数据挖掘技术的效率取决于完善的医疗数据的可用性。在我们目前的研究中,我们讨论了很少用于医学数据分析的分类器技术。也很少考虑疾病预测分析,例如乳腺癌预测,心脏病诊断,甲状腺预测和糖尿病。结果表明,决策树算法具有更好的准确性,因此非常适合疾病预测。

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