Enhancing Sarcopenia Prediction Through an Ensemble Learning Approach: Addressing Class Imbalance for Improved Clinical Diagnosis

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This study developed an advanced ensemble learning model aimed to improve the accuracy of predicting sarcopenia, a condition characterized by a gradual decline in muscle mass and strength, leading to increased disability and mortality. The study focused on enhancing model performance by combining various machine learning methods and addressing critical challenges, such as class imbalance and data complexity. Several foundational models were employed, including support vector machine, random forest, neural network, logistic regression, and decision tree. To address class imbalance, the adaptive synthetic sampling method was implemented, producing synthetic samples for the minority class to achieve a more balanced dataset. The data preprocessing stage included feature scaling and feature selection processes, utilizing recursive feature elimination to refine feature selection. Subsequently, a classifier selection algorithm was employed to select models that provided an optimal balance of diversity and performance. The effectiveness of the final ensemble model was evaluated using various metrics, such as accuracy, precision, recall, F1-score, and ROC AUC. The model achieved an accuracy of 88.5%, outperforming individual machine learning models and existing methods in the literature. These findings suggest that the classifier selection algorithm effectively addresses challenges in sarcopenia prediction, particularly in the case of imbalanced data. The model’s strong performance indicates its potential for use in clinical environments, where it can facilitate early diagnosis and improve intervention strategies for sarcopenia patients. This study advances the field of medical machine learning by demonstrating the utility of ensemble learning in healthcare prediction.