A stacking ensemble machine learning model to predict alpha-1 antitrypsin deficiency-associated liver disease clinical outcomes based on UK Biobank data

Abstract

Alpha-1 antitrypsin deficiency associated liver disease (AATD-LD) is a rare genetic disorder and not well-recognized. Predicting the clinical outcomes of AATD-LD and defining patients more likely to progress to advanced liver disease are crucial for better understanding AATD-LD progression and promoting timely medical intervention. We aimed to develop a tailored machine learning (ML) model to predict the disease progression of AATD-LD. This analysis was conducted through a stacking ensemble learning model by combining five different ML algorithms with 58 predictor variables using nested five-fold cross-validation with repetitions based on the UK Biobank data. Performance of the model was assessed through prediction accuracy, area under the receiver operating characteristic (AUROC), and area under the precision-recall curve (AUPRC). The importance of predictor contributions was evaluated through a feature importance permutation method. The proposed stacking ensemble ML model showed clinically meaningful accuracy and appeared superior to any single ML algorithms in the ensemble, e.g., the AUROC for AATD-LD was 68.1%, 75.9%, 91.2%, and 67.7% for all-cause mortality, liver-related death, liver transplant, and all-cause mortality or liver transplant, respectively. This work supports the use of ML to address the unanswered clinical questions with clinically meaningful accuracy using real-world data.

Enhancing the Internet of Medical Things (IoMT) Security with Meta-Learning: A Performance-Driven Approach for Ensemble Intrusion Detection Systems

“…Techniques such as bagging, boosting, and stacking are commonly employed in ensemble learning to enhance model performance by leveraging the strengths of individual models and mitigating their weaknesses [ 62 ]. Meta-ensemble methods, which involve combining multiple ensemble techniques, offer a comprehensive approach to adaptively adjust decision fusion strategies based on the characteristics of each base model, leading to superior predictive capabilities in tasks such as sentiment analysis and disease detection [ 36 , 63 ]. Additionally, the use of feature importance permutation methods and hyperparameter optimization in meta-learning on ensemble models allows for the evaluation of predictor contributions and the fine-tuning of model parameters to achieve optimal performance [ 43 , 63 ].…”

“…Meta-ensemble methods, which involve combining multiple ensemble techniques, offer a comprehensive approach to adaptively adjust decision fusion strategies based on the characteristics of each base model, leading to superior predictive capabilities in tasks such as sentiment analysis and disease detection [ 36 , 63 ]. Additionally, the use of feature importance permutation methods and hyperparameter optimization in meta-learning on ensemble models allows for the evaluation of predictor contributions and the fine-tuning of model parameters to achieve optimal performance [ 43 , 63 ]. Furthermore, meta-learning techniques in ensemble models facilitate the development of sophisticated approaches like stacking, where predictions from multiple base models are combined into a meta-learner model to improve overall accuracy and generalizability [ 57 ].…”