Hemoglobin value prediction with bayesian optimization assisted machine learning models

This study presents a framework for predicting hemoglobin (Hb) levels utilizing Bayesian optimization-assisted machine learning models, incorporating both time-domain and frequency-domain features derived from photoplethysmography (PPG) signals. Hemoglobin, a crucial protein for oxygen and carbon dioxide transport in the blood, has levels that indicate various health conditions, including anemia and diseases affecting red blood cell production. Traditional methods for measuring Hb levels are invasive, posing potential risks and discomfort. To address this, a dataset comprising PPG signals, along with demographic data (gender and age), was analyzed to predict Hb levels accurately. Our models employ support vector regression (SVR), artificial neural networks (ANNs), classification and regression trees (CART), and ensembles of trees (EoT) optimized through Bayesian optimization algorithm. The results demonstrated that incorporating age and gender as features significantly improved model performance, highlighting their importance in Hb level prediction. Among the tested models, ANN provided the best results, involving normalized raw signals, feature selection, and reduction methods. The model achieved a mean squared error (MSE) of 1.508, root mean squared error (RMSE) of 1.228, and R-squared (R²) of 0.226. This study's findings contribute to the growing body of research on non-invasive hemoglobin prediction, offering a potential tool for healthcare professionals and patients for convenient and risk-free Hb level monitoring.