Non-invasive prediction of hemoglobin level using machine learning techniques with the PPG signal's characteristics features

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dc.authorid0000-0002-8929-3473en_US
dc.authorid0000-0002-4380-9075en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorKavsaoğlu, Ahmet Reşit
dc.contributor.authorPolat, Kemal
dc.contributor.authorHariharan, Muthusamy
dc.date.accessioned2021-06-23T19:37:19Z
dc.date.available2021-06-23T19:37:19Z
dc.date.issued2015
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractHemoglobin can be measured normally after the analysis of the blood sample taken from the body and this measurement is named as invasive. Hemoglobin must continuously be measured to control the disease and its progression in people who go through hemodialysis and have diseases such as oligocythemia and anemia. This gives a perpetual feeling of pain to the people. This paper proposes a non-invasive method for the prediction of the hemoglobin using the characteristic features of the PPG signals and different machine learning algorithms. In this work, PPG signals from 33 people were included in 10 periods and 40 characteristic features were extracted from them. In addition to these features, gender information (male or female), height (as cm), weight (as kg) and age of each subjects were also considered as the features. Blood count and hemoglobin level were measured simultaneously by using the "Hemocue Hb-201TM" device. Using the different machine learning regression techniques (classification and regression trees - CART, least squares regression - LSR, generalized linear regression - GLR, multivariate linear regression - MVLR, partial least squares regression - PLSR, generalized regression neural network GRNN, MLP - multilayer perceptron, and support vector regression - SVR). RELIEFF feature selection (RFS) and correlation-based feature selection (CFS) were used to select the best features. Original features and selected features using RFS (10 features) and CFS (11 features) were used to predict the hemoglobin level using the different machine learning techniques. To evaluate the performance of the machine learning techniques, different performance measures such as mean absolute error - MAE, mean square error - MSE, R-2 (coefficient of determination), root mean square error - RMSE, Mean Absolute Percentage Error (MAPE) and Index of Agreement - IA were used. The promising results were obtained (MSE-0.0027) using the selected features by RFS and SVR. Hence, the proposed method may clinically be used to predict the hemoglobin level of human being clinically without taking and analyzing blood samples. (C) 2015 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2015.04.008
dc.identifier.endpage991en_US
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-84947128971en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage983en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2015.04.008
dc.identifier.urihttps://hdl.handle.net/20.500.12491/8142
dc.identifier.volume37en_US
dc.identifier.wosWOS:000365067800078en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPhotoplethysmography (PPG)en_US
dc.subjectHemoglobin (Hb)en_US
dc.subjectFeature Extractionen_US
dc.subjectRegression Modelsen_US
dc.subjectFeature Selectionen_US
dc.subjectPredictionen_US
dc.titleNon-invasive prediction of hemoglobin level using machine learning techniques with the PPG signal's characteristics featuresen_US
dc.typeArticleen_US

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