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@article{175208,
        author = {Shaik Areef and T Nithya and Shaik Arshad Valli and Shaik Nazeer and Shaik Rahamtulla},
        title = {Diabetes Detection Using Random Forest Algorithm By The Power Of Feature Engineering},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2024-2031},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175208},
        abstract = {Diabetes is a chronic disease affecting millions worldwide, and early detection plays a crucial role in managing and preventing its severe complications. This project presents a robust machine learning approach for diabetes prediction using the Random Forest algorithm enhanced by the power of feature engineering. By preprocessing and transforming raw clinical data, we extracted meaningful features that significantly improved the model's performance. The dataset used includes various health indicators such as glucose level, BMI, blood pressure, and insulin levels. Through systematic feature selection and engineering techniques, we identified the most influential attributes contributing to accurate predictions. The Random Forest model achieved a high training accuracy of 99% and a testing accuracy of 96%, demonstrating its reliability and generalizability. This work underscores the importance of combining machine learning with domain-specific feature engineering to build effective diagnostic tools in the healthcare sector.},
        keywords = {Diabetes Prediction, Machine Learning, Random Forest, Feature Engineering, Health Analysis, Data Preprocessing, Predictive Accuracy},
        month = {April},
        }