首页|Predicting chronic obstructive pulmonary disease (COPD) with optimized machine learning via leveraging comparative analysis of XGBoost and catboost
Predicting chronic obstructive pulmonary disease (COPD) with optimized machine learning via leveraging comparative analysis of XGBoost and catboost
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Abstract Chronic obstructive pulmonary disease (COPD) affects the health of millions of people worldwide. In this regard, this research tried to present a concept showing that optimized ML models can predict COPD based on the Exasen dataset. Further, the stochastic gamma process was explained as a continuous-time model with gamma-distributed increments and the compound Poisson process for modeling random jumps in Poisson events because of their relevance to modeling irregular patterns. The two algorithms that will be used in this study are extreme Gradient Boosting Classification, or XGBC, and CAT Boost Classification, CAT, both enhanced by the Artificial Rabbit Optimizer, or ARO, for hyperparameter tuning. The performances were measured concerning accuracy, Precision, Recall, and F1 score for COPD prediction. Surprisingly, both optimized models carried out excellent performance in COPD prediction; especially, the XGAR reached more than 0.910 accuracy in the training phase. Each model has a characteristic. Though XGBC yielded slightly high accuracy, the computational resource can be huge. In comparison, CAT had competitive results with XGBC, maybe with faster training times. The results here suggest that optimized XGBC and CAT are promising for COPD prediction on the Exasen dataset. Further studies will be required to confirm these results, especially for clinical applicability and generalizability across various populations. The contribution of this study involves the new application of ARO for the hyperparameter tuning of COPD prediction models with significant enhancements in the accuracies and performances of both XGBC and CAT algorithms on the Exasen dataset. ARO provides enhanced predictive capability by optimizing the critical model parameters and hence has the potential to enhance the effectiveness of ML in medical diagnostics. This work underlines the prospect of ML models with advanced optimization techniques for the betterment of COPD diagnosis, hence helping in its management and personalized treatment.
NETL
NSTL
