Ensemble Deep Learning for Enhanced Human Microbiome-Based Disease Prediction: A Comprehensive Analysis on Public Datasets

The importance of the human microbiome in determining one's health and its possible role in illness prediction have drawn more attention in recent years. However, the inherent challenges in microbiome data, which are characterized by small sample sizes and high dimensional characteristics, present significant obstacles to the effective use of machine learning techniques. This work presents a unique framework for ensemble deep learning-based disease prediction that combines supervised and unsupervised learning methods. To begin with, the samples are processed using unsupervised deep learning techniques to extract meaningful representations. Based on these deep representations, a disease score system is then developed, which functions as an informative feature for ensemble analysis. A score selection system is built with performance-boosting characteristics combined with the original samples to improve the ensemble. A gradient-boosting classifier is then trained on the resultant composite features to determine the patient's health state. For a thorough case study, the suggested ensemble deep learning approach is used on six publicly available datasets obtained from human microbiome profiling. The results show that our approach is effective in disease prediction using microbiome data, outperforming other algorithms in the process. This research contributes to the evolving landscape of predictive analytics in microbiome studies and offers a robust methodology for improving disease prediction accuracy.