Fine-Tunining the Future: Optimizing svm hyper-parameters or enhanced diabetes prediction

Abstract

Millions of people throughout the globe suffer from diabetes mellitus, a debilitating illness that increases the risk of severe complications and early death. To take preventative measures and tailor treatment to each individual's needs, it is essential to identify diabetes early and estimate risk accurately. This research provides a data-driven strategy for predicting diabetes based on SVM models. This work uses a large dataset, including clinical and demographic data from a wide range of people, including those with and without diabetes, to conduct our analysis. A prediction model that divides people into diabetes and non-diabetic groups based on their input attributes is constructed using the SVM algorithm. Engineers use feature selection and other engineering methods to improve the model's efficacy and readability. The results of the research show that the SVM algorithm is capable of producing reliable predictions of diabetes risk. Measures of the model's efficacy include its sensitivity to false positives, specificity in identifying true positives, and area under the Receiver Operating Characteristics curve (AUC-ROC). In addition, feature significance analysis improves the model's interpretability by illuminating the most critical risk variables for diabetes. The accuracy and interpretability of the proposed SVM-based diabetic prediction model are promising, making it a valuable tool for healthcare practitioners and policymakers to identify those at high risk of developing diabetes and modify preventative measures and interventions appropriately.