• Shruti Ajay SIngh
• Adit Loknath Poojari
• Femenca Noronha

Diabetes mellitus is a chronic disease that affects millions worldwide, making early prediction and risk analysis essential for effective management. Machine learning (ML) techniques have demonstrated significant efficiency in diagnosing diabetes using medical datasets. This research leverages Random Forest (RF) and Support Vector Machine (SVM) to predict diabetes based on clinical and lifestyle parameters. A publicly available diabetes dataset is pre-processed to remove inconsistencies and extract relevant features. Comparative analysis is conducted using evaluation metrics such as accuracy, precision, recall, F1-score, and AUC-ROC curve. The experimental results show that SVM outperforms RF in classification tasks. This study highlights the importance of feature selection and ML techniques in medical diagnostics, paving the way for more effective and efficient predictive models in healthcare

SVM (Support Vector Machine) Random Forest (RF), Diabetes, Prediction model.

"Diabetes Prediction and Risk Analysis using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.e175-e182, March-2025, Available :http://www.jetir.org/papers/JETIR2503434.pdf

"Diabetes Prediction and Risk Analysis using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppe175-e182, March-2025, Available at : http://www.jetir.org/papers/JETIR2503434.pdf