Adaptive Classification of Impending Human Heart, Kidney and Liver Failures Based on Measurable Blood-Related Parameters Using MIMO HANFA-ART with ACA Algorithms

Vincent Andrew Akpan; Oluwatosin Temidayo Omotehinwa; Joshua Babatunde Agbogun

doi:doi:10.11648/j.bs.20220803.14

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Adaptive Classification of Impending Human Heart, Kidney and Liver Failures Based on Measurable Blood-Related Parameters Using MIMO HANFA-ART with ACA Algorithms

Vincent Andrew Akpan, Oluwatosin Temidayo Omotehinwa, Joshua Babatunde Agbogun

Published in Biomedical Sciences (Volume 8, Issue 3)

Received: 19 April 2022 Accepted: 8 June 2022 Published: 31 August 2022

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Abstract

Heart, kidney and/or liver failure is a life-threatening condition that demands early detection as well as urgent medical attention and diagnosis based on the classification of their respective symptoms. This paper presents the application of multi-input multi-output hybrid adaptive neural-fuzzy algorithm based on adaptive resonant theory (MIMO HANFA-ART) with adaptive clustering algorithm (ACA) for the adaptive classification of the symptoms of impending human heart, kidney and liver failures based on measurable blood-related parameters obtained from hospitals in Akure metropolis of Ondo State, Nigeria. The ACA consist of an adaptive Gustafson and Kessel clustering (AG-KC) algorithm which is initialized by the K-means clustering algorithm. The 7 classes are: (i) Class 1: heart, (ii) Class 2: kidney, (iii) Class 3: liver, (iv) Class 4: kidney and liver, (v) Class 5: heart and liver, (vi) Class 6: heart and kidney, and (vii) Class 7: heart, kidney and liver. A total of 5888 data set with 16 attributes classified into 7 classes for 368 patients collected from 4 hospitals have been used for this investigation. Comparison of the MIMO HANFA-ART with ACA algorithms with neural-fuzzy classifier trained with the modified error back-propagation with momentum (M-EBPM) algorithm shows the efficiency and superior performance of the MIMO HANFA-ART with ACA algorithms for correct classification and prediction of the symptoms of impending heart, kidney and liver failure. MIMO HANFA-ART with ACA algorithms can be adapted and deployed for real-time online prediction and classification of the symptoms of heart, kidney and liver for early detection and medical attention using advanced biomedical electronics instrumentation techniques and Internet-of-Things (IoT) technologies.

Published in	Biomedical Sciences (Volume 8, Issue 3)
DOI	10.11648/j.bs.20220803.14
Page(s)	97-112
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Adaptive Clustering Algorithm (ACA), Adaptive Gustafson and Kessel Clustering (AG-KC), K-Means Clustering, Adaptive Classification, Blood-Related Parameters, M-EBPM, MIMO HANFA-ART, Neural-Fuzzy Systems

References

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APA Style

Vincent Andrew Akpan, Oluwatosin Temidayo Omotehinwa, Joshua Babatunde Agbogun. (2022). Adaptive Classification of Impending Human Heart, Kidney and Liver Failures Based on Measurable Blood-Related Parameters Using MIMO HANFA-ART with ACA Algorithms. Biomedical Sciences, 8(3), 97-112. https://doi.org/10.11648/j.bs.20220803.14

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ACS Style

Vincent Andrew Akpan; Oluwatosin Temidayo Omotehinwa; Joshua Babatunde Agbogun. Adaptive Classification of Impending Human Heart, Kidney and Liver Failures Based on Measurable Blood-Related Parameters Using MIMO HANFA-ART with ACA Algorithms. Biomed. Sci. 2022, 8(3), 97-112. doi: 10.11648/j.bs.20220803.14

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AMA Style

Vincent Andrew Akpan, Oluwatosin Temidayo Omotehinwa, Joshua Babatunde Agbogun. Adaptive Classification of Impending Human Heart, Kidney and Liver Failures Based on Measurable Blood-Related Parameters Using MIMO HANFA-ART with ACA Algorithms. Biomed Sci. 2022;8(3):97-112. doi: 10.11648/j.bs.20220803.14

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@article{10.11648/j.bs.20220803.14,
  author = {Vincent Andrew Akpan and Oluwatosin Temidayo Omotehinwa and Joshua Babatunde Agbogun},
  title = {Adaptive Classification of Impending Human Heart, Kidney and Liver Failures Based on Measurable Blood-Related Parameters Using MIMO HANFA-ART with ACA Algorithms},
  journal = {Biomedical Sciences},
  volume = {8},
  number = {3},
  pages = {97-112},
  doi = {10.11648/j.bs.20220803.14},
  url = {https://doi.org/10.11648/j.bs.20220803.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bs.20220803.14},
  abstract = {Heart, kidney and/or liver failure is a life-threatening condition that demands early detection as well as urgent medical attention and diagnosis based on the classification of their respective symptoms. This paper presents the application of multi-input multi-output hybrid adaptive neural-fuzzy algorithm based on adaptive resonant theory (MIMO HANFA-ART) with adaptive clustering algorithm (ACA) for the adaptive classification of the symptoms of impending human heart, kidney and liver failures based on measurable blood-related parameters obtained from hospitals in Akure metropolis of Ondo State, Nigeria. The ACA consist of an adaptive Gustafson and Kessel clustering (AG-KC) algorithm which is initialized by the K-means clustering algorithm. The 7 classes are: (i) Class 1: heart, (ii) Class 2: kidney, (iii) Class 3: liver, (iv) Class 4: kidney and liver, (v) Class 5: heart and liver, (vi) Class 6: heart and kidney, and (vii) Class 7: heart, kidney and liver. A total of 5888 data set with 16 attributes classified into 7 classes for 368 patients collected from 4 hospitals have been used for this investigation. Comparison of the MIMO HANFA-ART with ACA algorithms with neural-fuzzy classifier trained with the modified error back-propagation with momentum (M-EBPM) algorithm shows the efficiency and superior performance of the MIMO HANFA-ART with ACA algorithms for correct classification and prediction of the symptoms of impending heart, kidney and liver failure. MIMO HANFA-ART with ACA algorithms can be adapted and deployed for real-time online prediction and classification of the symptoms of heart, kidney and liver for early detection and medical attention using advanced biomedical electronics instrumentation techniques and Internet-of-Things (IoT) technologies.},
 year = {2022}
}

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TY  - JOUR
T1  - Adaptive Classification of Impending Human Heart, Kidney and Liver Failures Based on Measurable Blood-Related Parameters Using MIMO HANFA-ART with ACA Algorithms
AU  - Vincent Andrew Akpan
AU  - Oluwatosin Temidayo Omotehinwa
AU  - Joshua Babatunde Agbogun
Y1  - 2022/08/31
PY  - 2022
N1  - https://doi.org/10.11648/j.bs.20220803.14
DO  - 10.11648/j.bs.20220803.14
T2  - Biomedical Sciences
JF  - Biomedical Sciences
JO  - Biomedical Sciences
SP  - 97
EP  - 112
PB  - Science Publishing Group
SN  - 2575-3932
UR  - https://doi.org/10.11648/j.bs.20220803.14
AB  - Heart, kidney and/or liver failure is a life-threatening condition that demands early detection as well as urgent medical attention and diagnosis based on the classification of their respective symptoms. This paper presents the application of multi-input multi-output hybrid adaptive neural-fuzzy algorithm based on adaptive resonant theory (MIMO HANFA-ART) with adaptive clustering algorithm (ACA) for the adaptive classification of the symptoms of impending human heart, kidney and liver failures based on measurable blood-related parameters obtained from hospitals in Akure metropolis of Ondo State, Nigeria. The ACA consist of an adaptive Gustafson and Kessel clustering (AG-KC) algorithm which is initialized by the K-means clustering algorithm. The 7 classes are: (i) Class 1: heart, (ii) Class 2: kidney, (iii) Class 3: liver, (iv) Class 4: kidney and liver, (v) Class 5: heart and liver, (vi) Class 6: heart and kidney, and (vii) Class 7: heart, kidney and liver. A total of 5888 data set with 16 attributes classified into 7 classes for 368 patients collected from 4 hospitals have been used for this investigation. Comparison of the MIMO HANFA-ART with ACA algorithms with neural-fuzzy classifier trained with the modified error back-propagation with momentum (M-EBPM) algorithm shows the efficiency and superior performance of the MIMO HANFA-ART with ACA algorithms for correct classification and prediction of the symptoms of impending heart, kidney and liver failure. MIMO HANFA-ART with ACA algorithms can be adapted and deployed for real-time online prediction and classification of the symptoms of heart, kidney and liver for early detection and medical attention using advanced biomedical electronics instrumentation techniques and Internet-of-Things (IoT) technologies.
VL  - 8
IS  - 3
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Author Information

Vincent Andrew Akpan

Department of Biomedical Technology, The Federal University of Technology, Akure, Nigeria
Oluwatosin Temidayo Omotehinwa

Department of Mathematics and Computer Science, Federal University of Health Sciences, Otukpo, Nigeria
Joshua Babatunde Agbogun

Department of Computer Science and Mathematics, Godfrey Okoye University, Enugu, Nigeria

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APA Style

Vincent Andrew Akpan, Oluwatosin Temidayo Omotehinwa, Joshua Babatunde Agbogun. (2022). Adaptive Classification of Impending Human Heart, Kidney and Liver Failures Based on Measurable Blood-Related Parameters Using MIMO HANFA-ART with ACA Algorithms. Biomedical Sciences, 8(3), 97-112. https://doi.org/10.11648/j.bs.20220803.14

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ACS Style

Vincent Andrew Akpan; Oluwatosin Temidayo Omotehinwa; Joshua Babatunde Agbogun. Adaptive Classification of Impending Human Heart, Kidney and Liver Failures Based on Measurable Blood-Related Parameters Using MIMO HANFA-ART with ACA Algorithms. Biomed. Sci. 2022, 8(3), 97-112. doi: 10.11648/j.bs.20220803.14

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AMA Style

Vincent Andrew Akpan, Oluwatosin Temidayo Omotehinwa, Joshua Babatunde Agbogun. Adaptive Classification of Impending Human Heart, Kidney and Liver Failures Based on Measurable Blood-Related Parameters Using MIMO HANFA-ART with ACA Algorithms. Biomed Sci. 2022;8(3):97-112. doi: 10.11648/j.bs.20220803.14

Copy | Download

@article{10.11648/j.bs.20220803.14,
  author = {Vincent Andrew Akpan and Oluwatosin Temidayo Omotehinwa and Joshua Babatunde Agbogun},
  title = {Adaptive Classification of Impending Human Heart, Kidney and Liver Failures Based on Measurable Blood-Related Parameters Using MIMO HANFA-ART with ACA Algorithms},
  journal = {Biomedical Sciences},
  volume = {8},
  number = {3},
  pages = {97-112},
  doi = {10.11648/j.bs.20220803.14},
  url = {https://doi.org/10.11648/j.bs.20220803.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bs.20220803.14},
  abstract = {Heart, kidney and/or liver failure is a life-threatening condition that demands early detection as well as urgent medical attention and diagnosis based on the classification of their respective symptoms. This paper presents the application of multi-input multi-output hybrid adaptive neural-fuzzy algorithm based on adaptive resonant theory (MIMO HANFA-ART) with adaptive clustering algorithm (ACA) for the adaptive classification of the symptoms of impending human heart, kidney and liver failures based on measurable blood-related parameters obtained from hospitals in Akure metropolis of Ondo State, Nigeria. The ACA consist of an adaptive Gustafson and Kessel clustering (AG-KC) algorithm which is initialized by the K-means clustering algorithm. The 7 classes are: (i) Class 1: heart, (ii) Class 2: kidney, (iii) Class 3: liver, (iv) Class 4: kidney and liver, (v) Class 5: heart and liver, (vi) Class 6: heart and kidney, and (vii) Class 7: heart, kidney and liver. A total of 5888 data set with 16 attributes classified into 7 classes for 368 patients collected from 4 hospitals have been used for this investigation. Comparison of the MIMO HANFA-ART with ACA algorithms with neural-fuzzy classifier trained with the modified error back-propagation with momentum (M-EBPM) algorithm shows the efficiency and superior performance of the MIMO HANFA-ART with ACA algorithms for correct classification and prediction of the symptoms of impending heart, kidney and liver failure. MIMO HANFA-ART with ACA algorithms can be adapted and deployed for real-time online prediction and classification of the symptoms of heart, kidney and liver for early detection and medical attention using advanced biomedical electronics instrumentation techniques and Internet-of-Things (IoT) technologies.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Adaptive Classification of Impending Human Heart, Kidney and Liver Failures Based on Measurable Blood-Related Parameters Using MIMO HANFA-ART with ACA Algorithms
AU  - Vincent Andrew Akpan
AU  - Oluwatosin Temidayo Omotehinwa
AU  - Joshua Babatunde Agbogun
Y1  - 2022/08/31
PY  - 2022
N1  - https://doi.org/10.11648/j.bs.20220803.14
DO  - 10.11648/j.bs.20220803.14
T2  - Biomedical Sciences
JF  - Biomedical Sciences
JO  - Biomedical Sciences
SP  - 97
EP  - 112
PB  - Science Publishing Group
SN  - 2575-3932
UR  - https://doi.org/10.11648/j.bs.20220803.14
AB  - Heart, kidney and/or liver failure is a life-threatening condition that demands early detection as well as urgent medical attention and diagnosis based on the classification of their respective symptoms. This paper presents the application of multi-input multi-output hybrid adaptive neural-fuzzy algorithm based on adaptive resonant theory (MIMO HANFA-ART) with adaptive clustering algorithm (ACA) for the adaptive classification of the symptoms of impending human heart, kidney and liver failures based on measurable blood-related parameters obtained from hospitals in Akure metropolis of Ondo State, Nigeria. The ACA consist of an adaptive Gustafson and Kessel clustering (AG-KC) algorithm which is initialized by the K-means clustering algorithm. The 7 classes are: (i) Class 1: heart, (ii) Class 2: kidney, (iii) Class 3: liver, (iv) Class 4: kidney and liver, (v) Class 5: heart and liver, (vi) Class 6: heart and kidney, and (vii) Class 7: heart, kidney and liver. A total of 5888 data set with 16 attributes classified into 7 classes for 368 patients collected from 4 hospitals have been used for this investigation. Comparison of the MIMO HANFA-ART with ACA algorithms with neural-fuzzy classifier trained with the modified error back-propagation with momentum (M-EBPM) algorithm shows the efficiency and superior performance of the MIMO HANFA-ART with ACA algorithms for correct classification and prediction of the symptoms of impending heart, kidney and liver failure. MIMO HANFA-ART with ACA algorithms can be adapted and deployed for real-time online prediction and classification of the symptoms of heart, kidney and liver for early detection and medical attention using advanced biomedical electronics instrumentation techniques and Internet-of-Things (IoT) technologies.
VL  - 8
IS  - 3
ER  -

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