Hemoglobin value prediction with bayesian optimization assisted machine learning models

Koray Açıcı

Makale detayları

Hemoglobin value prediction with bayesian optimization assisted machine learning models

Yazarlar:

Koray Açıcı

Yayıncı:

Ankara University

Ana Başlıklar:

Information Systems (Other)

Dergi:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.66 Issue 2, pp.176-200

Belge Tipi:

Makale

Yayın Tarihi:

11/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001322646

pISSN:

1303-6009

Anahtar kelimeler:

Özet:

 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 studys 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.

 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 studys 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.

 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 studys 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.

 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 studys 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.

Makale detayları

Hemoglobin value prediction with bayesian optimization assisted machine learning models

Yazarlar: Koray Açıcı

Yayıncı: Ankara University

Ana Başlıklar: Information Systems (Other)

Dergi: Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.66 Issue 2, pp.176-200

Belge Tipi: Makale

Yayın Tarihi: 11/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001322646

pISSN: 1303-6009

hemoglobin

machine learning

regression

Sayı

Yazarlar:

Koray Açıcı

Yayıncı:

Ankara University

Ana Başlıklar:

Information Systems (Other)

Dergi:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.66 Issue 2, pp.176-200

Belge Tipi:

Makale

Yayın Tarihi:

11/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001322646

pISSN:

1303-6009