PREDICTIVE MODELING OF DIABETES RISK USING CLINICAL AND LIFESTYLE INDICATORS: A POPULATION-BASED DATA ANALYSIS
DOI:
https://doi.org/10.69980/x9jzzh17Keywords:
Diabetes prediction , Machine Learning, Risk AssesmentAbstract
Diabetes mellitus is an increasing worldwide health issue that needs to be addressed with proper measures of early detection and prevention. The purpose of the study is to design and test predictive equations of diabetes risk with the help of both clinical and lifestyle predictors based on the large population-based dataset containing 100,000 records. Examples of variables present in the dataset are age, gender, body mass index (BMI), hypertension, heart disease, smoking history, HbA1c level, and blood glucose level, and diabetes status is the outcome variable. The analytical design used was a cross-sectional design, which included data preprocessing, feature selection, and supervised machine learning. Several models, such as logistic regression, decision tree, random forest, and gradient boosting, were created and tested on performance measures, including accuracy, precision, recall, F1-score, and ROC-AUC. The findings point to the conclusion that the most predictive variables of diabetes are the levels of HbA1c and blood glucose, then BMI, and age. Ensemble models, especially gradient boosting, showed better predictive accuracy than the traditional methods. The results indicate the usefulness of using a combination of clinical and lifestyle data in predictive modeling to determine the risk of diabetes early. This method has huge consequences on preventive healthcare, allowing the timely intervention and better clinical decision-making.
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