The Applicability of Some Machine Learning Algorithms in the Prediction of Type 2 Diabetes

Vîrgolici Oana () and Tănăsescu Laura Gabriela ()
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Vîrgolici Oana: Academy of Economic Studies (ASE), Bucharest, Romania
Tănăsescu Laura Gabriela: Academy of Economic Studies (ASE), Bucharest, Romania

Proceedings of the International Conference on Business Excellence, 2024, vol. 18, issue 1, 246-257

Abstract: Type 2 diabetes is a metabolic disease that causes abnormal high levels of glucose in the blood. The pancreas is healthy, but the body doesn’t respond properly to its own insulin. The principal culprit is obesity, too much high fat tissue. So, measuring the body mass index or the waist circumference is a step to estimate the risk for this disease. Many people have no symptoms and the disease develops silently, causing serious problems with eyes, feet, heart and nerves. The prediction of diabetes is a very topical problem. In addition to medical guides, more and more machine learning models appear, trained on different databases. The purpose of these models is to predict diabetes, based on different parameters, not all of them coming from medical analyses. In the paper we present four diabetes prediction models, respectively based on the decision tree, support vector machine, logistic regression and k-nearest neighbors’ algorithms. All models are trained and tested on a database with approximately 65,000 records (divided into 70% for training and 30% for testing), which contains two blood markers (haemoglobin A1c and glucose), an anthropometric parameter (body mass index), age, gender and three categorical parameters (smoking status, hypertension, heart disease). We identify that Haemoglobin A1C and glucose are the most influential predictors. The models are evaluated in terms of accuracy score and confusion matrix and a ranking is presented at the end. The results obtained are very encouraging for all the presented models.

Keywords: type 2 diabetes mellitus; Machine Learning; Decision Tree (DT); Logistic Regression (LR); Support Vector Machine (SVM); k Nearest Neighbors (kNN) (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:poicbe:v:18:y:2024:i:1:p:246-257:n:1001

DOI: 10.2478/picbe-2024-0021

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