Comparing different machine learning techniques for diabetes risk prediction

Wenzhe Cai; Yifei Chen; Helin Wang

doi:10.1117/12.2673685

14 April 2023 Comparing different machine learning techniques for diabetes risk prediction

Wenzhe Cai, Yifei Chen, Helin Wang

Proceedings Volume 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022); 126130R (2023) https://doi.org/10.1117/12.2673685
Event: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 2022, Chongqing, China

Abstract

Diabetes is a very common disease, and many people in the world are poisoned by it. Diabetes will also cause some complication, such as diabetic foot, diabetic retinopathy, and even permanent loss of vision. At present, the most effective way to fight diabetes is early detection and early intervention. Restricting sugar intake as early as possible is the most economical approach. However, early diabetes often has no obvious symptoms, so it is very time-consuming and laborious for doctors to check one by one. Therefore, it is particularly important for an automated algorithm to assess the risk of diabetes change. This paper uses two machine learning methods and an artificial neuron network, popular frameworks to handle big data and built by python toolkits, to automatically make predictions of diabetes, and compares their performances of them in order to provide some insights into future diabetes prevention and treatments. The machine learning models in the paper are Support Vector Machine (SVM) and GaussianNB, both of which possess relatively good interpretability and classification ability. The deep learning model is a classical artificial neural network (ANN). The result shown by confusion matrices presents that the artificial neuron network has the best accuracy in prediction among the three models with a limited dataset, which is 0.736, and the SVM and GaussianNB are 0.72 and 0.735 respectively.

Citation Download Citation

Wenzhe Cai, Yifei Chen, and Helin Wang "Comparing different machine learning techniques for diabetes risk prediction", Proc. SPIE 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 126130R (14 April 2023); https://doi.org/10.1117/12.2673685

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KEYWORDS

Artificial neural networks

Data modeling

Education and training

Machine learning

Matrices

Glucose

Neurons

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