Non-invasive approach to predict the cholesterol level in blood using bioimpedance and neural network techniques

Mohktar, M.S. and Ibrahim, F. and Ismail, N.A. (2013) Non-invasive approach to predict the cholesterol level in blood using bioimpedance and neural network techniques. Biomedical Engineering - Applications, Basis and Communications, 25 (06). p. 1350046. ISSN 1793-7132, DOI https://doi.org/10.4015/s1016237213500464.

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Abstract

This paper presents a new non-invasive approach to predict the status of high total cholesterol (TC) level in blood using bioimpedance and the artificial neural network (ANN) techniques. The input parameters for the ANN model are acquired from a non-invasive bioelectrical impedance analysis (BIA) measurement technique. The measurement data were obtained from 260 volunteered participants. A total of 190 subject's data were used for the ANN training purpose and the remaining 70 subject's data were used for model testing. Six parameters from the BIA parameters were found to be significant predictors for TC level in blood using logistic regression analysis. The six input predictors for the ANN modeling are age, body mass index (BMI), body capacitance, basal metabolic rate, extracellular mass and lean body mass. Four ANN techniques such as the gradient descent with momentum, the resilient, the scaled conjugate gradient and the Levenberg-Marquardt were used and compared for predicting the high TC level in the blood. The finding showed that the resilient method was the best model with prediction accuracy, sensitivity, specificity and area under the curve value obtained from the test data were 82.9, 85.4, 79.3 and 0.83, respectively. © 2013 National Taiwan University.

Item Type:	Article
Funders:	UNSPECIFIED
Additional Information:	Export Date: 29 January 2014 Source: Scopus Art. No.: 1350046 CODEN: YIGOE Language of Original Document: English Correspondence Address: Mohktar, M.S.; Medical Informatics and Biological Micro-Electro-Mechanical Systems Specialized Laboratory, Department of Biomedical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia; email: masdayana@um.edu.my Chemicals/CAS: cholesterol, 57-88-5 Tradenames: Biodynamics Model 450 bioimpedance analyzer, Biodynamics Manufacturers: Biodynamics References: Natarajan, S., Glick, H., Criqui, M., Horowitz, D., Lipsitz, S.R., Kinosian, B., Cholesterol measures to identify and treat individuals at risk for coronary heart disease (2003) American J Preventive Med, 25, pp. 50-57; (2002) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Final Report in Circulation, 106, pp. 3143-3421. , Third Report of the National Cholesterol Education Program (NCEP) (American Heart Association, Greenville Avenue, Dallas; Birtcher, K.K., Ballantyne, C.M., Measurement of cholesterol a patient perspective (2004) Circulation, 110, pp. e296-e297; Lim, T., Ding, L., Zaki, M., Suleiman, A., Kew, S., Ismail, M., Maimunah, A., Rozita, H., Distribution of blood total cholesterol in a national sample of Malaysian adults (2000) Medical J Malaysia, 55, pp. 78-89; Hubert, H.B., Feinleib, M., McNamara, P.M., Castelli, W.P., Obesity as an independent risk factor for cardiovascular disease: A 26-year follow-up of participants in the Framingham heart study (1983) Circulation, 67, pp. 968-977; Taskinen, M.R., Diabetic dyslipidemia (2002) Atherosclerosis Supplements, 3, pp. 47-51; Stadtman, T.C., Preparation and assay of cholesterol and ergosterol (1957) Meth Enzymol, 3, pp. 392-394; Thadhani, R.I., Camargo, C.A., Xavier, R.J., Fang, L.S.T., Bazari, H., Atheroembolic renal failure after invasive procedures: Natural history based on 52 histologically proven cases (1995) Medicine, 74, pp. 350-358; Colín-Ramírez, E., Castillo-Martínez, L., Orea-Tejeda, A., Asensio Lafuente, E., Torres Villanueva, F., Rebollar Gonzalez, V., Narvaez David, R., Dorantes García J Body composition and echocardiographic abnormalities associated to anemia and volume overload in heart failure patients (2006) Clinical Nutrition, 25, pp. 746-757; Schoeller, D.A., Bioelectrical impedance analysis what does it measure? 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Uncontrolled Keywords:	Artificial neural network, Bioelectrical impedance, Logistic regression, Non-invasive, Total cholesterol, Bioelectrical impedance analysis, Logistic regression analysis, Logistic regressions, Neural network techniques, Scaled conjugate gradients, Total cholesterols, Blood, Cholesterol, Health, Neural networks, Regression analysis, Forecasting, adult, age, article, basal metabolic rate, body mass, cholesterol blood level, controlled study, electric capacitance, electrical equipment, extracellular space, female, human, human experiment, impedance, lean body weight, male, measurement accuracy, model, non invasive measurement, normal human, predictive value, reference value, sensitivity and specificity
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Engineering
Depositing User:	Mr Jenal S
Date Deposited:	24 Mar 2014 03:34
Last Modified:	01 Nov 2017 06:00
URI:	http://eprints.um.edu.my/id/eprint/9318

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