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@article{165417,
        author = {Dr.Kavita D.Hanabaratti and Ashwini Yadrami  and Madhushree Meti  and Sahana Sunkad  and Sanjana Kalyankar },
        title = {Ensemble Learning for Diabetes Prediction: A Comprehensive Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {11},
        number = {1},
        pages = {1210-1216},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165417},
        abstract = {Diabetes mellitus continues to be a major global health concern, underscoring the vital necessity of precise and timely prediction models. The goal of this project is to create a powerful predictive tool that uses ensemble machine learning techniques to categorize people who are at risk of diabetes based on important health indicators like age, family history, BMI, and blood glucose levels. Developing a complete solution that includes phases for data preprocessing, feature selection, model training, evaluation, and deployment is the aim. This work aims to achieve high accuracy, interpretability, and generalizability in diabetes prediction by utilizing an ensemble approach with a combination of Random Forest, Decision Tree, and Support Vector Classifier models. The ultimate aim is to provide healthcare practitioners with a trustworthy device for early discovery and negotiative techniques, therefore improving patient outcomes and reducing healthcare costs associated with diabetes management.},
        keywords = {Ensemble Machine Learning, Diagnostic analytics, Max voting, SVM, Random Forest classifier, Decision tree, Classification report},
        month = {},
        }