• Dhruv Garg
• Deepak Bhardwaj
• Dinesh Baghel

In India alone, the number of diabetes patients has crossed the mark of 100 million. Many parameters are responsible for a person getting affected by diabetes like excessive cholesterol level, sugar level, irregular production of insulin in the body, and much more. People with diabetes mellitus can suffer from various life-threatening diseases such as kidney damage, loss of sight, regulation of heartbeat, etc. The research delves into the development and evaluation of a data-driven diabetes prediction system employing machine learning (ML) algorithms. To predict the risk of diabetes onset, the proposed method uses a variety of patient parameters, including demographics, lifestyle choices, and clinical markers. The project uses the Support Vector Machine (SVM) as the preferred model for predicting diabetes in a person. The preferred kernel for the SVM is Linear. The significance of responsible and effective model creation is emphasized by the thorough examination of ethical issues and privacy concerns of sensitive health data. Additionally, the paper addresses the interpretability of ML models to enhance comprehension for healthcare professionals and patients. The proposed diabetes prediction system exhibits promising accuracy and reliability, demonstrating its potential as a valuable tool for early diabetes risk assessment. These results provide a substantial contribution to the growing corpus of knowledge about machine learning uses for healthcare and provide insightful information about how to integrate these systems in real-world clinical settings. The authors have tested various machine learning models and found SVM as the most reliable reaching an accuracy of 76.62%. A website framework has been used to provide inputs and predict diabetes. The dataset is provided by the ‘National Institute of Diabetes and Digestive and Kidney Diseases’ of female patients.

Support vector machine, diabetes mellitus, machine learning, early disease detection, Healthcare, and website.

"Diabetes Prediction Model Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.f787-f793, May-2024, Available :http://www.jetir.org/papers/JETIR2405588.pdf

"Diabetes Prediction Model Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppf787-f793, May-2024, Available at : http://www.jetir.org/papers/JETIR2405588.pdf