Prediction causes of diabetes detection using machine learning methods
DOI:
https://doi.org/10.34185/1562-9945-1-156-2025-05Keywords:
diabetes, prediction, machine learning, Decision Tree, Random Forest, Ada Boost, k-NN.Abstract
Diabetes is one of the most common chronic diseases in the world, affecting about 530 million people. The main causes of its occurrence include genetic predisposition, obesity, im-proper eating behavior, insulin resistance and bad habits. Early detection of the disease can prevent its development. Many symptoms of diabetes, such as dry mouth, frequent urination, blurred vision, weight loss, constant hunger, are not always immediately considered as signs of the disease. But these symptoms can be early indicators of high blood glucose levels. The paper analyzes the factors and causes that affect the risk of developing diabetes and makes predictions using the Decision Tree, Random Forest, k-NN and Ada Boost machine learning methods. The results are analyzed and the accuracy of the methods used is assessed. The re-sults obtained will allow for the detection of significantly more cases of diabetes before it oc-curs, early and effective treatment, and reduction of healthcare costs.
References
American Diabetes Association. Diagnosis and classification of diabetes mellitus // Diabe-tes Care. Clinical Practice Recommendations. 2012. Vol. 35 (Supplement_1). P. 64-71. URL: https://doi.org/10.2337/dc12-s064.
Larabi-Marie-Sainte S., Aburahmah L., Almohaini R., Saba T. Current techniques for dia-betes prediction: review and case study // Appl. Sci. 2019. Vol. 9, no.
URL: https://doi.org/10.3390/app9214604.
Zia U.A., Khan N. Predicting diabetes in medical datasets using machine learning tech-niques // Int. J. Sci. Eng. Res. 2017. Vol. 8(5). P. 257–267.
Neha D., Chande P., Neha M. A Study of Machine Learning Techniques for Diabetes Pre-diction // International Journal of Emerging Trends.in Engineering Research. 2022. Vol. 10, no. 2. P. 133-140. URL: https://doi.org/10.30534/ijeter/2022/181022022.
Kamadi, V., Allam A., Thummala, S. A computational intelligence technique for the effec-tive diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach // Applied Soft Computing, 2016. Vol.49, P.137-145. URL: https://doi.org/10.1016/j.asoc.2016.05.010.
Sydorenko K.V., Khomiak Т.V. Аnaliz prichyn ta prognozyvannya vyiavlennya tsykrovogo diabety metodom mashinnogo navchannya Decision Tree // The 6th International scientific and practical conference“Methodical and practical methods of creating inventions” (October 24 – 27, 2023), Sofia, Bulgaria. International Science Group. 2023. p. 266-271. (URL: https://doi.org/10.46299/ISG.2023.2.6).
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