Zulfian Azmi, Rina Julita, Novica Irawati, Sofyan Pariyasto, Ellanda Purwawijaya

COMPARATIVE ANALYSIS OF HYPERPARAMETER OPTIMIZATION TECHNIQUES ON LIGHTGBM FOR ASTHMA PREDICTION

Introduction

Comparative analysis of hyperparameter optimization techniques on lightgbm for asthma prediction. Evaluate LightGBM hyperparameter optimization (RandomizedSearchCV, Optuna, Bayesian) for asthma prediction. Bayesian Optimization excels with 78% accuracy, enhancing clinical decision support.

Abstract

This study presents a comparative study of hyperparameter optimization methods applied to the Light Gradient Boosting Machine (LightGBM) algorithm for asthma prediction. Traditional machine learning models often face limitations in accuracy and generalization capabilities due to suboptimal hyperparameter configurations. To address these challenges, this study evaluates and compares four approaches: Default LightGBM, RandomizedSearchCV, Optuna Optimization, and Bayesian Optimization. Experimental results show that Bayesian Optimization provides the best performance with an accuracy of 78%, a precision of 0.7778, a recall of 0.7778, an F1-score of 0.7778, and an ROC-AUC of 0.975. These findings emphasize the importance of selecting an appropriate optimization strategy to improve model performance in clinical prediction tasks. Overall, this study confirms the effectiveness of Bayesian Optimization in improving the predictive capabilities of LightGBM and provides an important contribution to the development of decision support systems in healthcare, particularly in the diagnosis and management of asthma

Review

This study presents a timely and relevant comparative analysis of hyperparameter optimization (HPO) techniques applied to LightGBM for asthma prediction, a critical area in clinical decision support. The authors effectively address the common challenge of suboptimal model performance stemming from unoptimized hyperparameters, systematically evaluating four distinct strategies: Default LightGBM, RandomizedSearchCV, Optuna, and Bayesian Optimization. The clear objective and direct comparative approach are commendable, offering valuable insights into improving the predictive capabilities of machine learning models in healthcare. The finding that Bayesian Optimization yields superior performance, with an impressive ROC-AUC of 0.975, strongly supports the paper's central premise regarding the importance of selecting an appropriate HPO strategy. The strength of this research lies in its rigorous comparative methodology and the practical implications of its findings. LightGBM is a robust and efficient algorithm often favored in medical applications, and the exploration of advanced HPO methods like Optuna and Bayesian Optimization alongside more traditional approaches provides a comprehensive benchmark. The reporting of standard metrics (accuracy, precision, recall, F1-score, and ROC-AUC) allows for a clear understanding of each method's performance across different aspects of prediction. The demonstration that a well-chosen HPO technique, specifically Bayesian Optimization, can significantly enhance model performance, is a crucial contribution to the development of more accurate and reliable predictive systems for diseases like asthma. While the abstract clearly highlights the benefits of Bayesian Optimization, a more detailed presentation of the underlying data characteristics (e.g., sample size, feature types) and the specific cross-validation strategy employed would strengthen the claims, particularly regarding the generalizability of the 78% accuracy. Future work could also benefit from exploring the computational cost associated with each optimization technique, as this is a practical consideration in clinical settings. Additionally, a discussion on the statistical significance of the performance differences between the HPO methods would further solidify the conclusions. Nevertheless, this study makes a significant contribution by underscoring the vital role of sophisticated HPO in clinical predictive modeling, providing a solid foundation for further research and practical application in asthma management.

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