A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction

IF 1.3 Q3 ENGINEERING, MULTIDISCIPLINARY
None Ajay Kumar, None Kamaldeep Kaur
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引用次数: 0

Abstract

Early detection of diabetes is crucial because of its incurable nature. Several diabetes prediction models have been developed using machine learning techniques (MLTs). The performance of MLTs varies for different accuracy measures. Thus, selecting appropriate MLTs for diabetes prediction is challenging. This paper proposes a multi-criteria decision-making (MCDM) based framework for evaluating MLTs applied to diabetes prediction. Initially, three MCDM methods—WSM, TOPSIS, and VIKOR—are used to determine the individual ranks of MLTs for diabetes prediction performance by using various comparable performance measures (PMs). Next, a fusion approach is used to determine the final rank of the MLTs. The proposed method is validated by assessing the performance of 10 MLTs on the Pima Indian diabetes dataset using eight evaluation metrics for diabetes prediction. Based on the final MCDM rankings, logistic regression is recommended for diabetes prediction modeling.
一个新的基于mcdm的框架推荐机器学习技术用于糖尿病预测
早期发现糖尿病是至关重要的,因为它无法治愈。使用机器学习技术(mlt)开发了几种糖尿病预测模型。对于不同的精度测量,mlt的性能是不同的。因此,选择合适的mlt用于糖尿病预测是具有挑战性的。本文提出了一种基于多准则决策(MCDM)的mlt评价框架,用于糖尿病预测。最初,使用三种MCDM方法- wsm, TOPSIS和vikor -通过使用各种可比较性能度量(pm)来确定mlt在糖尿病预测性能方面的个体排名。接下来,使用融合方法确定mlt的最终等级。通过使用8个评估指标评估10个mlt在皮马印第安人糖尿病数据集上的表现,验证了所提出的方法。根据最终的MCDM排名,建议使用逻辑回归进行糖尿病预测建模。
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来源期刊
CiteScore
2.80
自引率
0.00%
发文量
18
审稿时长
12 weeks
期刊介绍: The IJETI journal focus on the field of engineering and technology Innovation. And it publishes original papers including but not limited to the following fields: Automation Engineering Civil Engineering Control Engineering Electric Engineering Electronic Engineering Green Technology Information Engineering Mechanical Engineering Material Engineering Mechatronics and Robotics Engineering Nanotechnology Optic Engineering Sport Science and Technology Innovation Management Other Engineering and Technology Related Topics.
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