Computational Biology and Bioinformatics

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Bioinformatic System for Classification of Patient Response to Traditional and Modern Medicine Therapy in Niger Delta Region: A Machine Learning Approach

Received: 24 May 2022    Accepted: 4 July 2022    Published: 21 March 2023
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Abstract

The potential for Traditional Medicine (TM) to enhance human health and wellbeing is enormous. This facet of healthcare services is crucial. For mutual benefit, the systems of traditional medicine and western (modern) medicine must be combined. The main goal of merging biomedicals and healthcare information within the setting of primary health care services is to provide spaces for technology interchange in medical practice for data and knowledge-based development. As a result, a database for diseases and their likely causes, trigger patterns, and prospective treatments and cures will be created, facilitating faster access to healthcare and resulting in a more dependable and effective healthcare system. The integration of health data, information, and expertise is known as bioinformatics. Data is all about a particular patient history, such as symptoms, diagnoses, treatments, and results, are referred to as health information. In fact, practitioners of biomedical informatics put a lot of effort into spotting patterns in the data generated by bioinformatics in order to assess patients' health problems and develop effective healthcare procedures. Hence, it is crucial that the current healthcare system incorporate health bioinformatics. Traditional medicine (TM) needs solid, scientific evidence to support its effectiveness, it is significant to access perceptions and promotes the integration of both Traditional Medicine Practitioners (TMP) and Modern Medical Practitioners (MMP) in the society. Basically, this research paper adopts a quantitative research method through survey Questionnaire for perceptions and adoption of both TMPs and MMPs among practitioners in Akwa Ibom State, Nigeria. Correlation analysis was carried out on selected demographics variables using Spearman Correlation coefficient to test the information gathered about how traditional medicine and modern medicine interaction (drugs administration) in treatment of certain diseases. The research findings demonstrate that, the Spearman coefficient algorithm gave a 0.5% which indicating an average relationship which entails a requirement for further integration. Moreover, Machine Learning (ML) approach was adopted, the Linear Regression (LR) model was used to access the linear relationship existing within the number of visits (response) of patients on the four sickness that was identified in the statistical data obtained in order to do a comparison analysis of treatment length of time (tlot) based on weekly basis -Seven (7) days visits. The model enable prediction on future duration length of time of patient in (TMPs) health system given number of visits provided.

DOI 10.11648/j.cbb.20231101.11
Published in Computational Biology and Bioinformatics (Volume 11, Issue 1, June 2023)
Page(s) 1-12
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

Traditional Medicine Practitioners, Modern Medical Practitioners, Spearman Correlation Coefficient, Linear Regression, Efficacy, Bioinformatics

References
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[2] Awodele O, Agbaje E. O., Abiola O., Awodele D., Dolapo D. C (2012). Doctors’ attitudes towards the use of herbal medicine in Lagos, Nigeria. JOURNAL OF HERBAL MEDICINE 2 (2 0 1 2) pp 16–22.
[3] Agyei-Baffour. P, Kudolo. A, Quansah. D, and Boateng D. (2017). Integrating herbal medicine into mainstream healthcare in Ghana: clients’ acceptability, perceptions and disclosure of use. BMC Complementary and Alternative Medicine (2017) 17: 513 DOI 10.1186/s12906-017-2025-4.
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[6] Daaleman T. and Nease, D. (1994) “Patient Attitudes Regarding Physician Inquiry into Spiritual and Religious Issues”. Journal of Family Practice, Vol. 39: 564–567.
[7] Foster G. (1976). Disease etiologies in non-Western medical systems. American Anthropologist 78: 773–82.
[8] Hersh W. (2009). “A stimulus to define informatics and health information technology”. BMC Med Inform Decision Making 9: 24.
[9] In Y.., Tae-Min K., Myung S., Seong K. and Yeun-Jun C. (2013). Perspectives on Clinical Informatics: Integrating Large-Scale Clinical, Genomic, and Health Information for Clinical Care. Genomics and Informatics. Published online by Korea Genome Organization.
[10] Jamal A., McKenzie K and Clark M. (2009). The impact of health information technology on the quality of medical and health care: a systematic review. Health Information Management Journal Vol 38 No 3.
[11] James PB, Wardle J, Steel A et al, (2018). Traditional, complementary and alternative medicine use in Sub-Saharan Africa: a systematic review. BMJ Glob Health (2018); 3: e000895. doi: 10.1136/ bmjgh-2018-000895.
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[15] Olufunsho A, Kennedy I, Noel N. and John C. (2014). Traditional Medicine Policy and Regulation in Nigeria: An Index of Herbal Medicine Safety. Current Drug Safety, 2014, 9, pp 16-22.
[16] Okwu. D and Nnamd. F (2008). Afr. J. Traditional, Complementary and Alternative Medicines www.africanethnomedicines.net, Afr. J. Trad. CAM (2008) 5 (1): 194-200.
[17] Oreagba I, Oshikoya, and Amachree M.(2011). Herbal medicine use among urban residents in Lagos, Nigeria. BMC Complementary and Alternative Medicine, http://www.biomedcentral.com/1472-6882/11/117.11:117.
[18] Ragan. M (2014) Bioinformatics. In: Encyclopedia of Information Science and Technology, Third Edition (Khosrow-Pour M, Ed.) 1: 393-401.
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[20] Umoren I. Etuk U. Ekong A. and Udonyah K. (2021). Healthcare Logistics Optimization Framework for Efficient Supply Chain Management in Niger Delta Region of Nigeria, International Journal of Advanced Computer Science and Applications (IJACSA), 12 (4), http://dx.doi.org/10.14569/IJACSA.2021.0120475
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    Imeh Umoren, Victoria Essien, Ifeyinwa Arize. (2023). Bioinformatic System for Classification of Patient Response to Traditional and Modern Medicine Therapy in Niger Delta Region: A Machine Learning Approach. Computational Biology and Bioinformatics, 11(1), 1-12. https://doi.org/10.11648/j.cbb.20231101.11

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    Imeh Umoren; Victoria Essien; Ifeyinwa Arize. Bioinformatic System for Classification of Patient Response to Traditional and Modern Medicine Therapy in Niger Delta Region: A Machine Learning Approach. Comput. Biol. Bioinform. 2023, 11(1), 1-12. doi: 10.11648/j.cbb.20231101.11

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    AMA Style

    Imeh Umoren, Victoria Essien, Ifeyinwa Arize. Bioinformatic System for Classification of Patient Response to Traditional and Modern Medicine Therapy in Niger Delta Region: A Machine Learning Approach. Comput Biol Bioinform. 2023;11(1):1-12. doi: 10.11648/j.cbb.20231101.11

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  • @article{10.11648/j.cbb.20231101.11,
      author = {Imeh Umoren and Victoria Essien and Ifeyinwa Arize},
      title = {Bioinformatic System for Classification of Patient Response to Traditional and Modern Medicine Therapy in Niger Delta Region: A Machine Learning Approach},
      journal = {Computational Biology and Bioinformatics},
      volume = {11},
      number = {1},
      pages = {1-12},
      doi = {10.11648/j.cbb.20231101.11},
      url = {https://doi.org/10.11648/j.cbb.20231101.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20231101.11},
      abstract = {The potential for Traditional Medicine (TM) to enhance human health and wellbeing is enormous. This facet of healthcare services is crucial. For mutual benefit, the systems of traditional medicine and western (modern) medicine must be combined. The main goal of merging biomedicals and healthcare information within the setting of primary health care services is to provide spaces for technology interchange in medical practice for data and knowledge-based development. As a result, a database for diseases and their likely causes, trigger patterns, and prospective treatments and cures will be created, facilitating faster access to healthcare and resulting in a more dependable and effective healthcare system. The integration of health data, information, and expertise is known as bioinformatics. Data is all about a particular patient history, such as symptoms, diagnoses, treatments, and results, are referred to as health information. In fact, practitioners of biomedical informatics put a lot of effort into spotting patterns in the data generated by bioinformatics in order to assess patients' health problems and develop effective healthcare procedures. Hence, it is crucial that the current healthcare system incorporate health bioinformatics. Traditional medicine (TM) needs solid, scientific evidence to support its effectiveness, it is significant to access perceptions and promotes the integration of both Traditional Medicine Practitioners (TMP) and Modern Medical Practitioners (MMP) in the society. Basically, this research paper adopts a quantitative research method through survey Questionnaire for perceptions and adoption of both TMPs and MMPs among practitioners in Akwa Ibom State, Nigeria. Correlation analysis was carried out on selected demographics variables using Spearman Correlation coefficient to test the information gathered about how traditional medicine and modern medicine interaction (drugs administration) in treatment of certain diseases. The research findings demonstrate that, the Spearman coefficient algorithm gave a 0.5% which indicating an average relationship which entails a requirement for further integration. Moreover, Machine Learning (ML) approach was adopted, the Linear Regression (LR) model was used to access the linear relationship existing within the number of visits (response) of patients on the four sickness that was identified in the statistical data obtained in order to do a comparison analysis of treatment length of time (tlot) based on weekly basis -Seven (7) days visits. The model enable prediction on future duration length of time of patient in (TMPs) health system given number of visits provided.},
     year = {2023}
    }
    

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Author Information
  • Department of Computer Science, Faculty of Physical Sciences, Akwa Ibom State University, Mkpat Enin, Nigeria

  • Department of Computer Science, Faculty of Physical Sciences, Akwa Ibom State University, Mkpat Enin, Nigeria

  • Department of Computer Science, Faculty of Physical Sciences, Akwa Ibom State University, Mkpat Enin, Nigeria

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