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DMP_MI: an effective diabetes mellitus classification algorithm on imbalanced data with missing values

Wang, Qian; Cao, Weijia; Guo, Jiawei; Ren, Jiadong; Cheng, Yongqiang; Davis, Darryl N.

Authors

Qian Wang

Weijia Cao

Jiawei Guo

Jiadong Ren



Abstract

As a widely known chronic disease, diabetes mellitus is called a silent killer. It makes the body produce less insulin and causes increased blood sugar, which leads to many complications and affects the normal functioning of various organs, such as eyes, kidneys, and nerves. Although diabetes has attracted high attention in research, due to the existence of missing values and class imbalance in the data, the overall performance of diabetes classification using machine learning is relatively low. In this paper, we propose an effective P rediction algorithm for D iabetes M ellitus classification on I mbalanced data with M issing values ( DMP_MI ). First, the missing values are compensated by the Naïve Bayes (NB) method for data normalization. Then, an adaptive synthetic sampling method (ADASYN) is adopted to reduce the influence of class imbalance on the prediction performance. Finally, a random forest (RF) classifier is used to generate predictions and evaluated using comprehensive set of evaluation indicators. Experiments performed on Pima Indians diabetes dataset from the University of California at Irvine, Irvine (UCI) Repository, have demonstrated the effectiveness and superiority of our proposed DMP_MI.

Journal Article Type Article
Publication Date Jul 19, 2019
Journal IEEE Access
Print ISSN 2169-3536
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 7
Pages 102232-102238
APA6 Citation Wang, Q., Cao, W., Guo, J., Ren, J., Cheng, Y., & Davis, D. N. (2019). DMP_MI: an effective diabetes mellitus classification algorithm on imbalanced data with missing values. IEEE access : practical innovations, open solutions, 7, 102232-102238. https://doi.org/10.1109/access.2019.2929866
DOI https://doi.org/10.1109/access.2019.2929866
Keywords Diabetes mellitus prediction; Machine learning; Adaptive synthetic sampling; Diabetes: Classification algorithms; Prediction algorithms; Diseases; Medical diagnostic imaging
Publisher URL https://ieeexplore.ieee.org/document/8766801
Additional Information This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/

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Copyright Statement
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/





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