<![CDATA[Diabetes is a chronic disease that may lead to serious health complications if not detected and treated early. Early detection plays a crucial role in minimizing long-term risks. This study aims to classify diabetes cases using a machine-learning approach based on linear models. The models applied in this research include logistic regression, linear discriminant analysis (LDA), ridge classifier, and support vector machine (SVM) with a linear kernel. We preprocessed the dataset to ensure quality and consistency. We evaluated each model’s performance using accuracy, precision, recall, F1-score, and AUC-ROC. Experimental results show that the ridge classifier achieved the highest performance, followed by LDA and linear SVM, with comparable results. Logistic regression also performed reasonably well, albeit with slightly lower metrics. These findings indicate that the linear model can provide accurate and reliable classification in the task of predicting diabetes, contributing to the proof that this model can serve as the basis for a decision support system for early diabetes diagnosis in the healthcare sector.]]>
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