Identification of Risk Factors for Type 2 Diabetes Mellitus: A Machine Learning Approach.
Introduction: Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease that causes serious health problems worldwide. Multiple risk factors contribute to the development of this disease. Recently, researchers have used artificial intelligence and machine learning (ML) methods to identify these...
| Publicado en: | Lokman Hekim Health Sciences Vol. 6; no. 2; pp. 188 - 196 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
KARE Publishing
Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194639944&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194639944 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 27917835 N1MV jtl: Lokman Hekim Health Sciences issn: 27917835 maglogo: N pubinfo: dt: Jun2026 vid: 6 iid: 2 pid: 62027 pub: KARE Publishing artinfo: ui: 194639944 194639944 194639944 10.14744/lhhs.2026.40279 194639944 ppf: 188 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Identification of Risk Factors for Type 2 Diabetes Mellitus: A Machine Learning Approach. aug: au: Budak, Serkan Karacan, Yasemin Bacak, İsmail Özer, Şenay affil: Department of Health Care Services, Simav Vocational School of Health Services, Kütahya Health Sciences University, Kütahya, Türkiye sug: subj: Risk Assessment Diabetes Mellitus, Type 2 Risk Factors Machine Learning Prediction Models Human Analytic Research Interviews Disease Susceptibility Age Factors Sex Factors Hypertension Educational Status Turkiye Neural Networks (Computer) ROC Curve Support Vector Machine Random Forest Logistic Regression Power Analysis Hospitals Male Female Adult Middle Age Confidence Intervals Simple Random Sample Decision Trees Descriptive Statistics Data Analysis Software Odds Ratio Diabetic Patients Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Introduction: Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease that causes serious health problems worldwide. Multiple risk factors contribute to the development of this disease. Recently, researchers have used artificial intelligence and machine learning (ML) methods to identify these risk factors. This study aims to evaluate the risk factors for T2DM using ML methods. Methods: This analytical study was conducted over a 2-month period. Data were collected through face-to-face interviews using a personal information form. The obtained data were analyzed using different ML models and performance parameters such as F1 score, accuracy (ACC), and area under the curve (AUC), which represents the area under the receiver operating characteristic curve. Results: In this study, the most important risk factors for T2DM were identified as age, gender, high blood pressure, genetic predisposition, and education status. Moreover, seven different ML models were analyzed using F1 score, ACC, and AUC parameters, and support vector machine, random forest (RF), and logistic regression (LR) models provided the highest performance. Discussion and Conclusion: Accurate classification of T2DM risk factors is important for disease prevention and risk assessment in clinical practice. The results suggest that RF or LR models may affect populations with different sociocultural characteristics. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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