Citation

Hnin, Hsu Wai (2026) Prediction of Insulin Doses for Diabetes Patients in Hospital using Voting Classifier. Journal of Engineering Technology and Applied Physics, 8 (2). p. 98. ISSN 2682-8383

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Abstract

The diabetes patients in the hospital need to supply accurate insulin doselevels. This paper uses the93 KB diabetes dataset,which consistsof the 101766 diabetes patients. The dataset is transformedinto clean data using the data preprocessing techniques.The data is split into 80-20 train and test data. The four voting classifier models, such as two hard voting and two soft voting classifier models,are developedby using random forest, K nearest neighbors, extreme gradient boosting, and logistic regression. The data is trained into the four models,and the four models’ performanceis analyzedusing classification reports in metrics such as precision, recall, F1-score, and accuracy. According to 0.77 precision, 0.79 recall, 0.78 f1-score, and 0.79 accuracy, the SoftVoting Classifier model SV2 isgreater than the other three models,HV1, HV2, and SV1. According to accuracy,the SV2model is the best modelto predict insulin doselevels.

Item Type:	Article
Uncontrolled Keywords:	Diabetes- voting classifier, hard voting
Subjects:	R Medicine > R Medicine (General) > R858-859.7 Computer applications to medicine. Medical informatics
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	12 Oct 2026 03:04
Last Modified:	12 Oct 2026 03:04
URII:	http://shdl.mmu.edu.my/id/eprint/16898

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