Abstract
Diabetes is a prevalent chronic illness affecting a vast population worldwide. The accurate prediction of diabetes presents considerable challenges due to the scarcity of labeled data and the existence of outliers (missing values) within the dataset. Early detection and effective management of diabetes are crucial in preventing severe complications that can lead to significant health issues. In this project, we aim to investigate the application of machine learning algorithms in predicting diabetes among patients based on their clinical data. Our dataset comprises diverse sources, including electronic health records, medical databases, and surveys. Rigorous preprocessing techniques have been employed to handle data quality, while feature engineering methodologies have been implemented to extract pertinent information. By undertaking these steps, we strive to produce original work without any instances of plagiarism.
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