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Machine Learning Through Early Diabetes Detection: Evaluating Random Forest Classifier Performance
Abstract
Diabetes is among the life-threatening diseases affecting millions of people worldwide. Detection done early and accurate prediction of diabetes can significantly improve patient outcomes by enabling timely intervention and lifestyle modifications. In recent years, Machine Learning (ML) techniques have been extensively used in medical diagnostics due to their ability to analyze complex patterns in healthcare data. This study focuses on evaluating the performance of the Random Forest (RF) algorithm. The dataset including feature scaling, handling missing values and splitting into training and testing sets to ensure optimal mode performance. The results are evaluated based on performance metrics like Accuracy, Recall, Precision, and F-Measure that are derived from the confusion matrix. The experimental results proved that the best accuracy goes for Random Forest (RF).
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