Multiclass classification of metabolic conditions using fasting plasma levels of glucose and insulin

Abstract In clinical practice, plasma glucose concentrations in fasting and postprandial are measured to assess glucose metabolism and to diagnose diabetes. Plasma glucose and insulin concentrations in fasting and postprandial have been used to better characterize normal, prediabetic and diabetic co...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Altuve, Miguel [verfasserIn] Alvarez, Antonio J. [verfasserIn] Severeyn, Erika [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Classification Glucose Insulin Artificial neural network Support vector machine Random forests

Anmerkung:	© IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Health and Technology - Berlin : Springer, 2011, 11(2021), 4 vom: 15. Apr., Seite 953-962
Übergeordnetes Werk:	volume:11 ; year:2021 ; number:4 ; day:15 ; month:04 ; pages:953-962

Links:	Volltext

DOI / URN:	10.1007/s12553-021-00550-w

Katalog-ID:	SPR044597223

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520			\|a Abstract In clinical practice, plasma glucose concentrations in fasting and postprandial are measured to assess glucose metabolism and to diagnose diabetes. Plasma glucose and insulin concentrations in fasting and postprandial have been used to better characterize normal, prediabetic and diabetic conditions. In this paper, we seek to automatically recognize nine classes of metabolic conditions (three normal, three prediabetics, and three diabetics) by considering the age the patient and its fasting plasma glucose (FPG) and insulin (FPI) concentrations. Multinomial logistic regression (MLR), artificial neural network (ANN), support vector machine (SVM), decision tree (DT) and random forests (RF) were set for different attribute combinations (age, FPG and FPI). Accuracy, and macro-average and weighted-average measures of precision, recall and F1-score were employed to assess the performance of the classifiers. Accuracy and weighted-average of precision, recall and F1-score above 79% were obtained using an ANN and an RF with age, FPG and FPI as attributes. In terms of the weighted-average of F1, an ANN with FPG and FPI as attributes was the best classifier (weighted-average F1 = 81.50%). Age, FPG and FPI provided information to recognize the nine metabolic classes. Moreover, age helped to distinguish between two diabetic classes with overlapping glucose and insulin levels. Given the morbidity and mortality rate of metabolic diseases (Latin America counts for 26 million diabetic people and 10 million undiagnosed), the significance of this work lies in the conception of an automatic classifier for diagnosis support or preliminary screening in places with limited or non-existent health service delivery systems.
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700	1		\|a Severeyn, Erika \|e verfasserin \|4 aut
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allfields	10.1007/s12553-021-00550-w doi (DE-627)SPR044597223 (SPR)s12553-021-00550-w-e DE-627 ger DE-627 rakwb eng 610 ASE 44.09 bkl Altuve, Miguel verfasserin aut Multiclass classification of metabolic conditions using fasting plasma levels of glucose and insulin 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract In clinical practice, plasma glucose concentrations in fasting and postprandial are measured to assess glucose metabolism and to diagnose diabetes. Plasma glucose and insulin concentrations in fasting and postprandial have been used to better characterize normal, prediabetic and diabetic conditions. In this paper, we seek to automatically recognize nine classes of metabolic conditions (three normal, three prediabetics, and three diabetics) by considering the age the patient and its fasting plasma glucose (FPG) and insulin (FPI) concentrations. Multinomial logistic regression (MLR), artificial neural network (ANN), support vector machine (SVM), decision tree (DT) and random forests (RF) were set for different attribute combinations (age, FPG and FPI). Accuracy, and macro-average and weighted-average measures of precision, recall and F1-score were employed to assess the performance of the classifiers. Accuracy and weighted-average of precision, recall and F1-score above 79% were obtained using an ANN and an RF with age, FPG and FPI as attributes. In terms of the weighted-average of F1, an ANN with FPG and FPI as attributes was the best classifier (weighted-average F1 = 81.50%). Age, FPG and FPI provided information to recognize the nine metabolic classes. Moreover, age helped to distinguish between two diabetic classes with overlapping glucose and insulin levels. Given the morbidity and mortality rate of metabolic diseases (Latin America counts for 26 million diabetic people and 10 million undiagnosed), the significance of this work lies in the conception of an automatic classifier for diagnosis support or preliminary screening in places with limited or non-existent health service delivery systems. Classification (dpeaa)DE-He213 Glucose (dpeaa)DE-He213 Insulin (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Random forests (dpeaa)DE-He213 Alvarez, Antonio J. verfasserin aut Severeyn, Erika verfasserin aut Enthalten in Health and Technology Berlin : Springer, 2011 11(2021), 4 vom: 15. Apr., Seite 953-962 (DE-627)640089313 (DE-600)2581463-1 2190-7196 nnns volume:11 year:2021 number:4 day:15 month:04 pages:953-962 https://dx.doi.org/10.1007/s12553-021-00550-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 ASE AR 11 2021 4 15 04 953-962
spelling	10.1007/s12553-021-00550-w doi (DE-627)SPR044597223 (SPR)s12553-021-00550-w-e DE-627 ger DE-627 rakwb eng 610 ASE 44.09 bkl Altuve, Miguel verfasserin aut Multiclass classification of metabolic conditions using fasting plasma levels of glucose and insulin 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract In clinical practice, plasma glucose concentrations in fasting and postprandial are measured to assess glucose metabolism and to diagnose diabetes. Plasma glucose and insulin concentrations in fasting and postprandial have been used to better characterize normal, prediabetic and diabetic conditions. In this paper, we seek to automatically recognize nine classes of metabolic conditions (three normal, three prediabetics, and three diabetics) by considering the age the patient and its fasting plasma glucose (FPG) and insulin (FPI) concentrations. Multinomial logistic regression (MLR), artificial neural network (ANN), support vector machine (SVM), decision tree (DT) and random forests (RF) were set for different attribute combinations (age, FPG and FPI). Accuracy, and macro-average and weighted-average measures of precision, recall and F1-score were employed to assess the performance of the classifiers. Accuracy and weighted-average of precision, recall and F1-score above 79% were obtained using an ANN and an RF with age, FPG and FPI as attributes. In terms of the weighted-average of F1, an ANN with FPG and FPI as attributes was the best classifier (weighted-average F1 = 81.50%). Age, FPG and FPI provided information to recognize the nine metabolic classes. Moreover, age helped to distinguish between two diabetic classes with overlapping glucose and insulin levels. Given the morbidity and mortality rate of metabolic diseases (Latin America counts for 26 million diabetic people and 10 million undiagnosed), the significance of this work lies in the conception of an automatic classifier for diagnosis support or preliminary screening in places with limited or non-existent health service delivery systems. Classification (dpeaa)DE-He213 Glucose (dpeaa)DE-He213 Insulin (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Random forests (dpeaa)DE-He213 Alvarez, Antonio J. verfasserin aut Severeyn, Erika verfasserin aut Enthalten in Health and Technology Berlin : Springer, 2011 11(2021), 4 vom: 15. Apr., Seite 953-962 (DE-627)640089313 (DE-600)2581463-1 2190-7196 nnns volume:11 year:2021 number:4 day:15 month:04 pages:953-962 https://dx.doi.org/10.1007/s12553-021-00550-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 ASE AR 11 2021 4 15 04 953-962
allfields_unstemmed	10.1007/s12553-021-00550-w doi (DE-627)SPR044597223 (SPR)s12553-021-00550-w-e DE-627 ger DE-627 rakwb eng 610 ASE 44.09 bkl Altuve, Miguel verfasserin aut Multiclass classification of metabolic conditions using fasting plasma levels of glucose and insulin 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract In clinical practice, plasma glucose concentrations in fasting and postprandial are measured to assess glucose metabolism and to diagnose diabetes. Plasma glucose and insulin concentrations in fasting and postprandial have been used to better characterize normal, prediabetic and diabetic conditions. In this paper, we seek to automatically recognize nine classes of metabolic conditions (three normal, three prediabetics, and three diabetics) by considering the age the patient and its fasting plasma glucose (FPG) and insulin (FPI) concentrations. Multinomial logistic regression (MLR), artificial neural network (ANN), support vector machine (SVM), decision tree (DT) and random forests (RF) were set for different attribute combinations (age, FPG and FPI). Accuracy, and macro-average and weighted-average measures of precision, recall and F1-score were employed to assess the performance of the classifiers. Accuracy and weighted-average of precision, recall and F1-score above 79% were obtained using an ANN and an RF with age, FPG and FPI as attributes. In terms of the weighted-average of F1, an ANN with FPG and FPI as attributes was the best classifier (weighted-average F1 = 81.50%). Age, FPG and FPI provided information to recognize the nine metabolic classes. Moreover, age helped to distinguish between two diabetic classes with overlapping glucose and insulin levels. Given the morbidity and mortality rate of metabolic diseases (Latin America counts for 26 million diabetic people and 10 million undiagnosed), the significance of this work lies in the conception of an automatic classifier for diagnosis support or preliminary screening in places with limited or non-existent health service delivery systems. Classification (dpeaa)DE-He213 Glucose (dpeaa)DE-He213 Insulin (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Random forests (dpeaa)DE-He213 Alvarez, Antonio J. verfasserin aut Severeyn, Erika verfasserin aut Enthalten in Health and Technology Berlin : Springer, 2011 11(2021), 4 vom: 15. Apr., Seite 953-962 (DE-627)640089313 (DE-600)2581463-1 2190-7196 nnns volume:11 year:2021 number:4 day:15 month:04 pages:953-962 https://dx.doi.org/10.1007/s12553-021-00550-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 ASE AR 11 2021 4 15 04 953-962
allfieldsGer	10.1007/s12553-021-00550-w doi (DE-627)SPR044597223 (SPR)s12553-021-00550-w-e DE-627 ger DE-627 rakwb eng 610 ASE 44.09 bkl Altuve, Miguel verfasserin aut Multiclass classification of metabolic conditions using fasting plasma levels of glucose and insulin 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract In clinical practice, plasma glucose concentrations in fasting and postprandial are measured to assess glucose metabolism and to diagnose diabetes. Plasma glucose and insulin concentrations in fasting and postprandial have been used to better characterize normal, prediabetic and diabetic conditions. In this paper, we seek to automatically recognize nine classes of metabolic conditions (three normal, three prediabetics, and three diabetics) by considering the age the patient and its fasting plasma glucose (FPG) and insulin (FPI) concentrations. Multinomial logistic regression (MLR), artificial neural network (ANN), support vector machine (SVM), decision tree (DT) and random forests (RF) were set for different attribute combinations (age, FPG and FPI). Accuracy, and macro-average and weighted-average measures of precision, recall and F1-score were employed to assess the performance of the classifiers. Accuracy and weighted-average of precision, recall and F1-score above 79% were obtained using an ANN and an RF with age, FPG and FPI as attributes. In terms of the weighted-average of F1, an ANN with FPG and FPI as attributes was the best classifier (weighted-average F1 = 81.50%). Age, FPG and FPI provided information to recognize the nine metabolic classes. Moreover, age helped to distinguish between two diabetic classes with overlapping glucose and insulin levels. Given the morbidity and mortality rate of metabolic diseases (Latin America counts for 26 million diabetic people and 10 million undiagnosed), the significance of this work lies in the conception of an automatic classifier for diagnosis support or preliminary screening in places with limited or non-existent health service delivery systems. Classification (dpeaa)DE-He213 Glucose (dpeaa)DE-He213 Insulin (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Random forests (dpeaa)DE-He213 Alvarez, Antonio J. verfasserin aut Severeyn, Erika verfasserin aut Enthalten in Health and Technology Berlin : Springer, 2011 11(2021), 4 vom: 15. Apr., Seite 953-962 (DE-627)640089313 (DE-600)2581463-1 2190-7196 nnns volume:11 year:2021 number:4 day:15 month:04 pages:953-962 https://dx.doi.org/10.1007/s12553-021-00550-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 ASE AR 11 2021 4 15 04 953-962
allfieldsSound	10.1007/s12553-021-00550-w doi (DE-627)SPR044597223 (SPR)s12553-021-00550-w-e DE-627 ger DE-627 rakwb eng 610 ASE 44.09 bkl Altuve, Miguel verfasserin aut Multiclass classification of metabolic conditions using fasting plasma levels of glucose and insulin 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract In clinical practice, plasma glucose concentrations in fasting and postprandial are measured to assess glucose metabolism and to diagnose diabetes. Plasma glucose and insulin concentrations in fasting and postprandial have been used to better characterize normal, prediabetic and diabetic conditions. In this paper, we seek to automatically recognize nine classes of metabolic conditions (three normal, three prediabetics, and three diabetics) by considering the age the patient and its fasting plasma glucose (FPG) and insulin (FPI) concentrations. Multinomial logistic regression (MLR), artificial neural network (ANN), support vector machine (SVM), decision tree (DT) and random forests (RF) were set for different attribute combinations (age, FPG and FPI). Accuracy, and macro-average and weighted-average measures of precision, recall and F1-score were employed to assess the performance of the classifiers. Accuracy and weighted-average of precision, recall and F1-score above 79% were obtained using an ANN and an RF with age, FPG and FPI as attributes. In terms of the weighted-average of F1, an ANN with FPG and FPI as attributes was the best classifier (weighted-average F1 = 81.50%). Age, FPG and FPI provided information to recognize the nine metabolic classes. Moreover, age helped to distinguish between two diabetic classes with overlapping glucose and insulin levels. Given the morbidity and mortality rate of metabolic diseases (Latin America counts for 26 million diabetic people and 10 million undiagnosed), the significance of this work lies in the conception of an automatic classifier for diagnosis support or preliminary screening in places with limited or non-existent health service delivery systems. Classification (dpeaa)DE-He213 Glucose (dpeaa)DE-He213 Insulin (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Random forests (dpeaa)DE-He213 Alvarez, Antonio J. verfasserin aut Severeyn, Erika verfasserin aut Enthalten in Health and Technology Berlin : Springer, 2011 11(2021), 4 vom: 15. Apr., Seite 953-962 (DE-627)640089313 (DE-600)2581463-1 2190-7196 nnns volume:11 year:2021 number:4 day:15 month:04 pages:953-962 https://dx.doi.org/10.1007/s12553-021-00550-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 ASE AR 11 2021 4 15 04 953-962
language	English
source	Enthalten in Health and Technology 11(2021), 4 vom: 15. Apr., Seite 953-962 volume:11 year:2021 number:4 day:15 month:04 pages:953-962
sourceStr	Enthalten in Health and Technology 11(2021), 4 vom: 15. Apr., Seite 953-962 volume:11 year:2021 number:4 day:15 month:04 pages:953-962
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Classification Glucose Insulin Artificial neural network Support vector machine Random forests
dewey-raw	610
isfreeaccess_bool	false
container_title	Health and Technology
authorswithroles_txt_mv	Altuve, Miguel @@aut@@ Alvarez, Antonio J. @@aut@@ Severeyn, Erika @@aut@@
publishDateDaySort_date	2021-04-15T00:00:00Z
hierarchy_top_id	640089313
dewey-sort	3610
id	SPR044597223
language_de	englisch
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author	Altuve, Miguel
spellingShingle	Altuve, Miguel ddc 610 bkl 44.09 misc Classification misc Glucose misc Insulin misc Artificial neural network misc Support vector machine misc Random forests Multiclass classification of metabolic conditions using fasting plasma levels of glucose and insulin
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topic_title	610 ASE 44.09 bkl Multiclass classification of metabolic conditions using fasting plasma levels of glucose and insulin Classification (dpeaa)DE-He213 Glucose (dpeaa)DE-He213 Insulin (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Random forests (dpeaa)DE-He213
topic	ddc 610 bkl 44.09 misc Classification misc Glucose misc Insulin misc Artificial neural network misc Support vector machine misc Random forests
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title	Multiclass classification of metabolic conditions using fasting plasma levels of glucose and insulin
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title_full	Multiclass classification of metabolic conditions using fasting plasma levels of glucose and insulin
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author_browse	Altuve, Miguel Alvarez, Antonio J. Severeyn, Erika
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title_sort	multiclass classification of metabolic conditions using fasting plasma levels of glucose and insulin
title_auth	Multiclass classification of metabolic conditions using fasting plasma levels of glucose and insulin
abstract	Abstract In clinical practice, plasma glucose concentrations in fasting and postprandial are measured to assess glucose metabolism and to diagnose diabetes. Plasma glucose and insulin concentrations in fasting and postprandial have been used to better characterize normal, prediabetic and diabetic conditions. In this paper, we seek to automatically recognize nine classes of metabolic conditions (three normal, three prediabetics, and three diabetics) by considering the age the patient and its fasting plasma glucose (FPG) and insulin (FPI) concentrations. Multinomial logistic regression (MLR), artificial neural network (ANN), support vector machine (SVM), decision tree (DT) and random forests (RF) were set for different attribute combinations (age, FPG and FPI). Accuracy, and macro-average and weighted-average measures of precision, recall and F1-score were employed to assess the performance of the classifiers. Accuracy and weighted-average of precision, recall and F1-score above 79% were obtained using an ANN and an RF with age, FPG and FPI as attributes. In terms of the weighted-average of F1, an ANN with FPG and FPI as attributes was the best classifier (weighted-average F1 = 81.50%). Age, FPG and FPI provided information to recognize the nine metabolic classes. Moreover, age helped to distinguish between two diabetic classes with overlapping glucose and insulin levels. Given the morbidity and mortality rate of metabolic diseases (Latin America counts for 26 million diabetic people and 10 million undiagnosed), the significance of this work lies in the conception of an automatic classifier for diagnosis support or preliminary screening in places with limited or non-existent health service delivery systems. © IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2021
abstractGer	Abstract In clinical practice, plasma glucose concentrations in fasting and postprandial are measured to assess glucose metabolism and to diagnose diabetes. Plasma glucose and insulin concentrations in fasting and postprandial have been used to better characterize normal, prediabetic and diabetic conditions. In this paper, we seek to automatically recognize nine classes of metabolic conditions (three normal, three prediabetics, and three diabetics) by considering the age the patient and its fasting plasma glucose (FPG) and insulin (FPI) concentrations. Multinomial logistic regression (MLR), artificial neural network (ANN), support vector machine (SVM), decision tree (DT) and random forests (RF) were set for different attribute combinations (age, FPG and FPI). Accuracy, and macro-average and weighted-average measures of precision, recall and F1-score were employed to assess the performance of the classifiers. Accuracy and weighted-average of precision, recall and F1-score above 79% were obtained using an ANN and an RF with age, FPG and FPI as attributes. In terms of the weighted-average of F1, an ANN with FPG and FPI as attributes was the best classifier (weighted-average F1 = 81.50%). Age, FPG and FPI provided information to recognize the nine metabolic classes. Moreover, age helped to distinguish between two diabetic classes with overlapping glucose and insulin levels. Given the morbidity and mortality rate of metabolic diseases (Latin America counts for 26 million diabetic people and 10 million undiagnosed), the significance of this work lies in the conception of an automatic classifier for diagnosis support or preliminary screening in places with limited or non-existent health service delivery systems. © IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2021
abstract_unstemmed	Abstract In clinical practice, plasma glucose concentrations in fasting and postprandial are measured to assess glucose metabolism and to diagnose diabetes. Plasma glucose and insulin concentrations in fasting and postprandial have been used to better characterize normal, prediabetic and diabetic conditions. In this paper, we seek to automatically recognize nine classes of metabolic conditions (three normal, three prediabetics, and three diabetics) by considering the age the patient and its fasting plasma glucose (FPG) and insulin (FPI) concentrations. Multinomial logistic regression (MLR), artificial neural network (ANN), support vector machine (SVM), decision tree (DT) and random forests (RF) were set for different attribute combinations (age, FPG and FPI). Accuracy, and macro-average and weighted-average measures of precision, recall and F1-score were employed to assess the performance of the classifiers. Accuracy and weighted-average of precision, recall and F1-score above 79% were obtained using an ANN and an RF with age, FPG and FPI as attributes. In terms of the weighted-average of F1, an ANN with FPG and FPI as attributes was the best classifier (weighted-average F1 = 81.50%). Age, FPG and FPI provided information to recognize the nine metabolic classes. Moreover, age helped to distinguish between two diabetic classes with overlapping glucose and insulin levels. Given the morbidity and mortality rate of metabolic diseases (Latin America counts for 26 million diabetic people and 10 million undiagnosed), the significance of this work lies in the conception of an automatic classifier for diagnosis support or preliminary screening in places with limited or non-existent health service delivery systems. © IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2021
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title_short	Multiclass classification of metabolic conditions using fasting plasma levels of glucose and insulin
url	https://dx.doi.org/10.1007/s12553-021-00550-w
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author2	Alvarez, Antonio J. Severeyn, Erika
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doi_str	10.1007/s12553-021-00550-w
up_date	2024-07-04T01:28:24.016Z
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Multiclass classification of metabolic conditions using fasting plasma levels of glucose and insulin

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