Pushpa Negi
ABSTRACT
Abstract - The issue of diabetes is a significant and urgent matter in global health, necessitating the development of precise predictive models to facilitate early intervention and effective management. The objective of this study is to assess the effectiveness of different feature selection techniques in improving the accuracy of diabetes prediction when employing the Random Forest algorithm. The present study evaluates the impact of three feature selection methods, namely Recursive Feature Elimination (RFE), Mutual Information, and L1 Regularization (Lasso), on the performance of the model. This study employs a comprehensive dataset obtained from Kaggle, which includes various attributes such as age, blood pressure, and laboratory measurements. The primary objective of this research is to systematically evaluate and compare the predictive performance of Random Forest models when integrated with different feature selection techniques. The evaluated metrics encompass accuracy, precision, recall, and F1-Score. The findings demonstrate significant enhancements in the accuracy of predictions when employing feature selection techniques. Specifically, the utilization of L1 Regularization (Lasso) resulted in the highest level of accuracy, reaching 98.45%. Furthermore, the features chosen by each method provide insight into the variables that have the greatest impact on predicting diabetes. This study offers significant findings regarding the significance of feature selection in healthcare applications, thereby enabling more accurate and effective diagnosis and treatment of diabetes. The results of this study not only make a valuable contribution to improving the accuracy of diabetes prediction, but also highlight the importance of carefully selecting relevant features when developing predictive models for healthcare datasets. This study promotes the need for additional investigation into feature selection methodologies and their implementation in the field of healthcare analytics, offering potential advantages in the realms of disease prediction and management.
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