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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.


Qian Wang

Weijia Cao

Jiawei Guo

Jiadong Ren

Yongqiang Cheng

Darryl N. Davis


© 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved. 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 Prediction algorithm for Diabetes Mellitus classification on Imbalanced data with Missing 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.


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, 7, 102232-102238.

Journal Article Type Article
Acceptance Date Jul 14, 2019
Online Publication Date Jul 19, 2019
Publication Date Jul 19, 2019
Deposit Date Aug 9, 2019
Publicly Available Date Aug 9, 2019
Journal IEEE Access
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 7
Pages 102232-102238
Keywords Diabetes mellitus prediction; Machine learning; Adaptive synthetic sampling; Diabetes: Classification algorithms; Prediction algorithms; Diseases; Medical diagnostic imaging
Public URL
Publisher URL
Additional Information This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see


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