Naïve Bayes based Summarizing Ruleset in Prediction of Diabetes Mellitus using Magnum Opus

J. Omana, M. Moorthi
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Abstract

Diabetes mellitus is the deficiency that is widely spreading nowadays. Manually diagnosing a person with diabetes is more complicated. If diabetes is not treated early it may lead to severe complications. We focus on Electronic Medical Records (EMR) to find out the factors that represent a patient with the risk of developing diabetes. We apply Apriori, Éclat and OPUS association rule mining techniques to generate the risk factors that occur frequently will help greatly in predicting diabetes. These frequent risk factors of each technique are subject to Naïve Bayes with which the chances for developing diabetes mellitus is predicted and the efficiency of each is obtained with respect to Success probability. In evaluating and comparing the previous techniques, OPUS is found to be efficient in predicting the factors that have a high risk of developing diabetes mellitus.
Naïve基于贝叶斯的总结规则集在Magnum Opus预测糖尿病中的应用
糖尿病是当今普遍存在的一种疾病。手动诊断糖尿病患者更为复杂。如果糖尿病不及早治疗,可能会导致严重的并发症。我们专注于电子医疗记录(EMR),以找出代表患者患糖尿病风险的因素。我们应用Apriori、Éclat和OPUS关联规则挖掘技术生成频繁发生的危险因素,对预测糖尿病有很大帮助。每种技术的这些常见危险因素都服从Naïve贝叶斯,用它来预测发生糖尿病的机会,并根据成功概率获得每种技术的效率。通过对以往技术的评价和比较,发现OPUS在预测糖尿病高危因素方面是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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