<![CDATA[Asthma is one of the most prevalent chronic respiratory diseases worldwide, affecting more than 300 million people, and its early prediction is essential for timely clinical intervention. A major obstacle in data-driven asthma prediction is the severe class imbalance of large-scale clinical datasets, which biases conventional classifiers toward the majority (non-asthma) class. This study proposes an Improved LightGBM that integrates three components: Hybrid SMOTE-ENN to correct class imbalance and remove noisy boundary samples, SHAP-based feature selection to retain the most informative attributes, and Bayesian Optimization for hyperparameter tuning. A Kaggle-derived asthma dataset (409,216 SMOTE-balanced training records and 59,672 test records over 23 encoded clinical features) was used. Hybrid SMOTE-ENN reduced a 40,000-sample working set to 8,322 cleaned, balanced instances; SHAP selected 13 of 23 features; and Bayesian Optimization produced the optimal configuration (best cross-validation accuracy 92.20%). On the balanced hold-out test set the proposed Improved LightGBM achieved an accuracy of 93.87%, precision of 0.9447, recall of 0.9359, F1-score of 0.9403, and ROC-AUC of 0.9839, clearly surpassing the LightGBM Bayesian-Optimization baseline reported in the main reference (78% accuracy, ROC-AUC 0.975). Evaluation on the original imbalanced test distribution (accuracy 72.83%, ROC-AUC 0.6392) transparently reflects the difficulty of severely imbalanced real-world clinical data. The results show that combining Hybrid SMOTE-ENN, SHAP feature selection, and Bayesian Optimization yields a more accurate, interpretable, and discriminative asthma-prediction model]]>
