<![CDATA[The increasing prevalence of diabetes mellitus highlights the need for accurate early detection methods. This study proposes a classification model for diabetes prediction using non-linear machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (K-NN). The dataset, obtained from Kaggle, includes clinical features such as glucose levels, BMI, blood pressure, and insulin. The methodology comprises data preprocessing, partitioning the data into training and testing sets, and evaluating the model’s using accuracy, precision, recall, and F1-score. Experimental results indicate that the Random Forest algorithm achieved the highest performance, followed by SVM and K-NN. We attribute Random Forest’s superior performance to its robustness in handling complex patterns and minimizing overfitting. We expect this research to contribute to developing practical early detection tools for diabetes, thereby supporting timely and data-driven medical decision-making.]]>
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