Multi-Model Fusion for Predicting Respiratory Flow and Metabolomic Analysis of Exhaled Breath Using Machine Learning

This study addresses the challenge of predicting respiratory flow and performing metabolomic analysis of exhaled breath by leveraging advanced machine learning techniques. Utilizing a comprehensive dataset collected under diverse environmental conditions, we evaluate the performance of various machine learning models, including Decision Tree, Random Forest, Support Vector Machine (SVM), Gradient Boosting, K-Nearest Neighbors (KNN), and XGBoost. The dataset encompasses detailed measurements of respiratory flow rate and exhaled breath metabolites, collected from subjects in different activity states. We implement feature extraction methods, including Principal Component Analysis (PCA) for dimensionality reduction, to handle the high-dimensional metabolomic data and identify significant metabolites. Our findings indicate that multi-model fusion approaches considerably enhance prediction accuracy and robustness, overcoming common issues such as overfitting, computational complexity, and physiological variability. This research highlights the potential of machine learning in developing reliable, non-invasive respiratory monitoring systems and advanced metabolomic analyses, contributing to improved health monitoring and personalized healthcare solutions.