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@article{190396,
        author = {Tushar N. Charde and Piyush V. Bankar and Gauri G. Thakre and Prasanna P. Gode and Manisha G. Vaidya},
        title = {Intelligent Diabetes Prediction System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {6372-6375},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190396},
        abstract = {This project presents an intelligent diabetes prediction system designed to support early detection and preventive healthcare. The system evaluates an individual’s risk of diabetes using key health parameters such as glucose level, blood pressure, BMI, insulin level, age, number of pregnancies, and diabetes pedigree function. A supervised machine learning model was trained on a publicly available dataset of anonymized patient records, with data preprocessing techniques applied to enhance accuracy and reliability. The system accepts user inputs, processes them through the trained model, and provides a clear binary prediction indicating diabetic or non-diabetic status. The primary objective is to assist individuals and healthcare providers in identifying high-risk cases at an early stage, enabling timely intervention and improved healthcare decision-making.},
        keywords = {Diabetes Prediction, Machine Learning, Supervised Learning, Healthcare Analytics, Data Preprocessing, Risk Assessment, Predictive, Web-Based Application. solution aims to support preventive healthcare by providing an accurate, accessible, and user-friendly decision-support tool for diabetes risk assessment.},
        month = {January},
        }