Performance Comparison of Improved Machine Learning Algorithms Based on Bayesian Optimization in High-dimensional and Unbalanced COPD Data

Abstract

Abstract Background and objective: Early identification of individuals at high risk of chronic obstructive pulmonary disease (COPD) is crucial for reducing related mortality rates and economic burden. However, conventional machine learning (ML) models have limitations when making predictions using COPD data that exhibit high-dimensional and unbalanced characteristics. Therefore, to address this issue, this study developed a well-performing Bayesian optimization (BO)-ML hybrid model combined with variable screening and resampling technology to construct a COPD risk prediction model. Methods: We collected a sample of 4,747 COPD cases with no missing data from the 2019 COPD Surveillance project in Shanxi Province, and extracted 34 potentially relevant variables from the dataset. Firstly, we used the Smoothly Clipped Absolute Deviation (SCAD) method to select variables associated with COPD. Secondly, we oversampling the unbalanced data using Synthetic Minority Over-sampling Technique (SMOTE) algorithm. Thirdly, we construct risk prediction models in the training set using four BO-improved ML models, including BO-Decision Tree (DT), BO-Naive Bayes (NB), BO-Support Vector Machine (SVM) and BO-K-nearest neighbor (KNN). Finally, the predictive performance of the combined models is tested and evaluated. Result: The SCAD method was used to select 14 variables specifically associated with COPD from a dataset of 34 features. After applying the SMOTE resampling method, the ratio of COPD patients to non-COPD patients in the dataset of this study was balanced at 1:1. In the construction process of the four ML models, this study utilized BO algorithm to identify their optimal hyperparameters. Furthermore, in the comparison of model performance, this study found that combining BO-ML hybrid models with data balancing techniques can improve their performance. Specifically, the combination of SMOTE and BO-NB demonstrated stable performance and attained high scores in the comprehensive evaluation index, with AUC and G-means values of 0.770 and 0.696 respectively. Conclusion: Despite the challenges posed by high dimensionality, redundancy, and class imbalance in data set, the BO-NB model, when integrated with SCAD and SMOTE, has exhibited excellent performance in accurately identifying individuals at a high risk of COPD. It provides early warnings to clinical doctors, helping them take timely preventive measures.