Enhancing Diabetes Risk Assessment in PIMA Indians: A Machine Learning Approach Using AdaBoost
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Abstract
In this study, AdaBoost methodology was employed to predict the likelihood of diabetes development among 768 PIMA Indians, utilizing their demographic and health records. The data underwent standardization, feature selection, missing value handling, and outlier rejection as part of the preparation process. By applying the AdaBoost classifier, the research aimed to assess diabetes risk, with evaluation metrics including accuracy, precision, recall, and F1 score. The results demonstrate the efficacy of AdaBoost in reliably predicting diabetes risk, holding significant implications for the early detection and prevention of diabetes among PIMA Indians.
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