Comprehensive data optimization and risk prediction framework: machine learning methods for inflammatory bowel disease prediction based on the human gut microbiome data

Abstract

Over the past decade, the prevalence of inflammatory bowel disease (IBD) has significantly increased, making early detection crucial for improving patient survival rates. Medical research suggests that changes in the human gut microbiome are closely linked to IBD onset, playing a critical role in its prediction. However, the current gut microbiome data often exhibit missing values and high dimensionality, posing challenges to the accuracy of predictive algorithms. To address these issues, we proposed the comprehensive data optimization and risk prediction framework (CDORPF), an ensemble learning framework designed to predict IBD risk based on the human gut microbiome, aiding early diagnosis. The framework comprised two main components: data optimization and risk prediction. The data optimization module first employed triple optimization imputation (TOI) to impute missing data while preserving the biological characteristics of the microbiome. It then utilized importance-weighted variational autoencoder (IWVAE) to reduce redundant information from the high-dimensional microbiome data. This process resulted in a complete, low-dimensional representation of the data, laying the foundation for improved algorithm efficiency and accuracy. In the risk prediction module, the optimized data was classified using a random forest (RF) model, and hyperparameters were globally optimized using improved aquila optimizer (IAO), which incorporated multiple strategies. Experimental results on IBD-related gut microbiome datasets showed that the proposed framework achieved classification accuracy, recall, and F1 scores exceeding 0.9, outperforming comparison models and serving as a valuable tool for predicting IBD onset risk.