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首页> 外文期刊>International journal of knowledge-based and intelligent engineering systems >A data mining scheme for detection and classification of diabetes mellitus using voting expert strategy
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A data mining scheme for detection and classification of diabetes mellitus using voting expert strategy

机译：一种使用投票专家策略对糖尿病进行检测和分类的数据挖掘方案

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In this work, an efficient scheme has been proposed for the computer-aided detection of the wide-spread disease diabetes. This scheme involves certain data mining techniques for the purpose of detecting the chances of diabetes by looking into a patient’s medical record. This work attempts to classify the nature of diabetes (Type-I and Type-II) as well. It also tries to determine the level of risk associated presently with the affected patient. Four different algorithms namely decision tree, Naive Bayes, support vector machine (SVM), and Adaboost-M1 have been used for the purpose of labeling the records as either diabetic or non-diabetic . A comparison strategy is then followed to adopt the best scheme among these through the voting expert. The proposed work gives satisfactory diagnosis result when compared to the ground-truth data. Overall accuracy rate of 95% is achieved through k -fold cross-validation (k = 10 ) method. Comparison of the proposed work with other state-of-the-art schemes has also been performed that favors the said work.
机译：在这项工作中，已经提出了一种有效的方案，用于计算机辅助检测广泛传播的糖尿病。该方案涉及某些数据挖掘技术，目的是通过查看患者的病历来检测糖尿病的机会。这项工作还试图对糖尿病的性质（I型和II型）进行分类。它还尝试确定当前与受影响患者相关的风险水平。为了将记录标记为糖尿病或非糖尿病，已使用了四种不同的算法，即决策树，朴素贝叶斯，支持向量机（SVM）和Adaboost-M1。然后遵循比较策略，以通过投票专家在其中采用最佳方案。与地面真实数据相比，所提出的工作给出了令人满意的诊断结果。通过k倍交叉验证（k = 10）方法可实现95％的总体准确率。还完成了拟议工作与其他最先进方案的比较，从而使上述工作更为有利。

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