A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms

Samuel Ekene Nnebe; Nora Augusta Ozemoya Okoh; Adetokunbo Mac Gregor John-Otumu; Emmanuel Osaze Oshoiribhor

doi:doi:10.11648/j.ajai.20190301.12

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A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms

Samuel Ekene Nnebe, Nora Augusta Ozemoya Okoh, Adetokunbo Mac Gregor John-Otumu, Emmanuel Osaze Oshoiribhor

Published in American Journal of Artificial Intelligence (Volume 3, Issue 1)

Received: 16 June 2019 Accepted: 13 July 2019 Published: 13 August 2019

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Abstract

Lassa fever is an acute viral haemorrhagic fever that is awfully infectious through infected rodents in the mastomysnatalensis species that are complex reservoirs capable of excreting the virus through their urine, saliva, excreta and other body fluids to man. The virus is a single stranded RNA virus belonging to the arenaviridae family. It presents no definite signs or symptoms and clinical analysis is often problematic especially at the early onset of the disease. Accurate diagnosis requires highly specialized laboratories, which are expensive and not readily available to the entire populace. Early diagnosis and treatment of Lassa fever is very vital for survival. In this study, we identified that fuzzy logic and rule-based techniques are the only artificial intelligence supported approach that has been used to develop an expert system for diagnosing the dreaded Lassa fever as an alternative to laboratory methodology. It is noted that rule-based is not an efficient technique in the designing expert systems based on its shortcomings such as opaque relations between rules, ineffective search strategy, and its inability to learn; while the fuzzy based technique does not also support the ability to learn but good in areas such as knowledge representation, uncertainty tolerance, imprecision tolerance, and explanation ability. Based on these information gathered, the authors decided to design a hybridized intelligent framework driven by the integration of Neural Network (NN), Fuzzy logic (FL) and Case Based Reasoning (CBR) based on their individual strengths put together in order to proffer a quick and reliable diagnosis for Lassa fever infection using observed clinical symptoms that could aid medical practitioners in decision making.

Published in	American Journal of Artificial Intelligence (Volume 3, Issue 1)
DOI	10.11648/j.ajai.20190301.12
Page(s)	9-16
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), 2019. Published by Science Publishing Group

Keywords

Intelligence, Hybrid Model, Neuro-fuzzy CBR, Expert System, Lassa Fever

References

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APA Style

Samuel Ekene Nnebe, Nora Augusta Ozemoya Okoh, Adetokunbo Mac Gregor John-Otumu, Emmanuel Osaze Oshoiribhor. (2019). A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms. American Journal of Artificial Intelligence, 3(1), 9-16. https://doi.org/10.11648/j.ajai.20190301.12

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ACS Style

Samuel Ekene Nnebe; Nora Augusta Ozemoya Okoh; Adetokunbo Mac Gregor John-Otumu; Emmanuel Osaze Oshoiribhor. A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms. Am. J. Artif. Intell. 2019, 3(1), 9-16. doi: 10.11648/j.ajai.20190301.12

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AMA Style

Samuel Ekene Nnebe, Nora Augusta Ozemoya Okoh, Adetokunbo Mac Gregor John-Otumu, Emmanuel Osaze Oshoiribhor. A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms. Am J Artif Intell. 2019;3(1):9-16. doi: 10.11648/j.ajai.20190301.12

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@article{10.11648/j.ajai.20190301.12,
  author = {Samuel Ekene Nnebe and Nora Augusta Ozemoya Okoh and Adetokunbo Mac Gregor John-Otumu and Emmanuel Osaze Oshoiribhor},
  title = {A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms},
  journal = {American Journal of Artificial Intelligence},
  volume = {3},
  number = {1},
  pages = {9-16},
  doi = {10.11648/j.ajai.20190301.12},
  url = {https://doi.org/10.11648/j.ajai.20190301.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20190301.12},
  abstract = {Lassa fever is an acute viral haemorrhagic fever that is awfully infectious through infected rodents in the mastomysnatalensis species that are complex reservoirs capable of excreting the virus through their urine, saliva, excreta and other body fluids to man.  The virus is a single stranded RNA virus belonging to the arenaviridae family. It presents no definite signs or symptoms and clinical analysis is often problematic especially at the early onset of the disease. Accurate diagnosis requires highly specialized laboratories, which are expensive and not readily available to the entire populace. Early diagnosis and treatment of Lassa fever is very vital for survival. In this study, we identified that fuzzy logic and rule-based techniques are the only artificial intelligence supported approach that has been used to develop an expert system for diagnosing the dreaded Lassa fever as an alternative to laboratory methodology. It is noted that rule-based is not an efficient technique in the designing expert systems based on its shortcomings such as opaque relations between rules, ineffective search strategy, and its inability to learn; while the fuzzy based technique does not also support the ability to learn but good in areas such as knowledge representation, uncertainty tolerance, imprecision tolerance, and explanation ability. Based on these information gathered, the authors decided to design a hybridized intelligent framework driven by the integration of Neural Network (NN), Fuzzy logic (FL) and Case Based Reasoning (CBR) based on their individual strengths put together in order to proffer a quick and reliable diagnosis for Lassa fever infection using observed clinical symptoms that could aid medical practitioners in decision making.},
 year = {2019}
}

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TY  - JOUR
T1  - A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms
AU  - Samuel Ekene Nnebe
AU  - Nora Augusta Ozemoya Okoh
AU  - Adetokunbo Mac Gregor John-Otumu
AU  - Emmanuel Osaze Oshoiribhor
Y1  - 2019/08/13
PY  - 2019
N1  - https://doi.org/10.11648/j.ajai.20190301.12
DO  - 10.11648/j.ajai.20190301.12
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 9
EP  - 16
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20190301.12
AB  - Lassa fever is an acute viral haemorrhagic fever that is awfully infectious through infected rodents in the mastomysnatalensis species that are complex reservoirs capable of excreting the virus through their urine, saliva, excreta and other body fluids to man.  The virus is a single stranded RNA virus belonging to the arenaviridae family. It presents no definite signs or symptoms and clinical analysis is often problematic especially at the early onset of the disease. Accurate diagnosis requires highly specialized laboratories, which are expensive and not readily available to the entire populace. Early diagnosis and treatment of Lassa fever is very vital for survival. In this study, we identified that fuzzy logic and rule-based techniques are the only artificial intelligence supported approach that has been used to develop an expert system for diagnosing the dreaded Lassa fever as an alternative to laboratory methodology. It is noted that rule-based is not an efficient technique in the designing expert systems based on its shortcomings such as opaque relations between rules, ineffective search strategy, and its inability to learn; while the fuzzy based technique does not also support the ability to learn but good in areas such as knowledge representation, uncertainty tolerance, imprecision tolerance, and explanation ability. Based on these information gathered, the authors decided to design a hybridized intelligent framework driven by the integration of Neural Network (NN), Fuzzy logic (FL) and Case Based Reasoning (CBR) based on their individual strengths put together in order to proffer a quick and reliable diagnosis for Lassa fever infection using observed clinical symptoms that could aid medical practitioners in decision making.
VL  - 3
IS  - 1
ER  -

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Author Information

Samuel Ekene Nnebe

Department of Computer Science, Ambrose Alli University, Ekpoma, Nigeria
Nora Augusta Ozemoya Okoh

Department of Computer Science, Ambrose Alli University, Ekpoma, Nigeria
Adetokunbo Mac Gregor John-Otumu

Department of Computer Science, Ambrose Alli University, Ekpoma, Nigeria
Emmanuel Osaze Oshoiribhor

Department of Computer Science, Ambrose Alli University, Ekpoma, Nigeria

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APA Style

Samuel Ekene Nnebe, Nora Augusta Ozemoya Okoh, Adetokunbo Mac Gregor John-Otumu, Emmanuel Osaze Oshoiribhor. (2019). A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms. American Journal of Artificial Intelligence, 3(1), 9-16. https://doi.org/10.11648/j.ajai.20190301.12

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ACS Style

Samuel Ekene Nnebe; Nora Augusta Ozemoya Okoh; Adetokunbo Mac Gregor John-Otumu; Emmanuel Osaze Oshoiribhor. A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms. Am. J. Artif. Intell. 2019, 3(1), 9-16. doi: 10.11648/j.ajai.20190301.12

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AMA Style

Samuel Ekene Nnebe, Nora Augusta Ozemoya Okoh, Adetokunbo Mac Gregor John-Otumu, Emmanuel Osaze Oshoiribhor. A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms. Am J Artif Intell. 2019;3(1):9-16. doi: 10.11648/j.ajai.20190301.12

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@article{10.11648/j.ajai.20190301.12,
  author = {Samuel Ekene Nnebe and Nora Augusta Ozemoya Okoh and Adetokunbo Mac Gregor John-Otumu and Emmanuel Osaze Oshoiribhor},
  title = {A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms},
  journal = {American Journal of Artificial Intelligence},
  volume = {3},
  number = {1},
  pages = {9-16},
  doi = {10.11648/j.ajai.20190301.12},
  url = {https://doi.org/10.11648/j.ajai.20190301.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20190301.12},
  abstract = {Lassa fever is an acute viral haemorrhagic fever that is awfully infectious through infected rodents in the mastomysnatalensis species that are complex reservoirs capable of excreting the virus through their urine, saliva, excreta and other body fluids to man.  The virus is a single stranded RNA virus belonging to the arenaviridae family. It presents no definite signs or symptoms and clinical analysis is often problematic especially at the early onset of the disease. Accurate diagnosis requires highly specialized laboratories, which are expensive and not readily available to the entire populace. Early diagnosis and treatment of Lassa fever is very vital for survival. In this study, we identified that fuzzy logic and rule-based techniques are the only artificial intelligence supported approach that has been used to develop an expert system for diagnosing the dreaded Lassa fever as an alternative to laboratory methodology. It is noted that rule-based is not an efficient technique in the designing expert systems based on its shortcomings such as opaque relations between rules, ineffective search strategy, and its inability to learn; while the fuzzy based technique does not also support the ability to learn but good in areas such as knowledge representation, uncertainty tolerance, imprecision tolerance, and explanation ability. Based on these information gathered, the authors decided to design a hybridized intelligent framework driven by the integration of Neural Network (NN), Fuzzy logic (FL) and Case Based Reasoning (CBR) based on their individual strengths put together in order to proffer a quick and reliable diagnosis for Lassa fever infection using observed clinical symptoms that could aid medical practitioners in decision making.},
 year = {2019}
}

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TY  - JOUR
T1  - A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms
AU  - Samuel Ekene Nnebe
AU  - Nora Augusta Ozemoya Okoh
AU  - Adetokunbo Mac Gregor John-Otumu
AU  - Emmanuel Osaze Oshoiribhor
Y1  - 2019/08/13
PY  - 2019
N1  - https://doi.org/10.11648/j.ajai.20190301.12
DO  - 10.11648/j.ajai.20190301.12
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 9
EP  - 16
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20190301.12
AB  - Lassa fever is an acute viral haemorrhagic fever that is awfully infectious through infected rodents in the mastomysnatalensis species that are complex reservoirs capable of excreting the virus through their urine, saliva, excreta and other body fluids to man.  The virus is a single stranded RNA virus belonging to the arenaviridae family. It presents no definite signs or symptoms and clinical analysis is often problematic especially at the early onset of the disease. Accurate diagnosis requires highly specialized laboratories, which are expensive and not readily available to the entire populace. Early diagnosis and treatment of Lassa fever is very vital for survival. In this study, we identified that fuzzy logic and rule-based techniques are the only artificial intelligence supported approach that has been used to develop an expert system for diagnosing the dreaded Lassa fever as an alternative to laboratory methodology. It is noted that rule-based is not an efficient technique in the designing expert systems based on its shortcomings such as opaque relations between rules, ineffective search strategy, and its inability to learn; while the fuzzy based technique does not also support the ability to learn but good in areas such as knowledge representation, uncertainty tolerance, imprecision tolerance, and explanation ability. Based on these information gathered, the authors decided to design a hybridized intelligent framework driven by the integration of Neural Network (NN), Fuzzy logic (FL) and Case Based Reasoning (CBR) based on their individual strengths put together in order to proffer a quick and reliable diagnosis for Lassa fever infection using observed clinical symptoms that could aid medical practitioners in decision making.
VL  - 3
IS  - 1
ER  -

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