Diabetes prediction and analysis using machine learning models

Yunjiu Li; Helin Wang; Zhirui Ye; Haina Zhou

doi:10.1117/12.2672671

1 March 2023 Diabetes prediction and analysis using machine learning models

Yunjiu Li, Helin Wang, Zhirui Ye, Haina Zhou

Proceedings Volume 12596, International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022); 1259615 (2023) https://doi.org/10.1117/12.2672671
Event: International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), 2022, Changsha, China

Abstract

Diabetes is a very serious worldwide chronic disease that affects people's life and health. Patients require insulin injections to maintain blood sugar balance exogenously. Methods to detect diabetes are time-consuming and labor-intensive. With the popularity of machine learning algorithms, we expect to predict and analyze diabetes through deep learning methods. In this paper, we utilize machine learning methods for data analysis and prediction. Our method was tested on public datasets and found that the random forest algorithm performed best, and that BMI and gender were the most important factors affecting the prevalence of diabetes.

Citation Download Citation

Yunjiu Li, Helin Wang, Zhirui Ye, and Haina Zhou "Diabetes prediction and analysis using machine learning models", Proc. SPIE 12596, International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), 1259615 (1 March 2023); https://doi.org/10.1117/12.2672671

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