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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

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.

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

APA Style

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.

ACS 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

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

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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