Expert Systems and Epidemiological Surveillance for Tuberculosis: Innovative Tools for Disease Prevention and Control
Expert Systems and Epidemiological Surveillance for Tuberculosis: Innovative Tools for Disease Prevention and Control |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-3 |
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| Year of Publication : 2024 | ||
| Author : Laberiano Andrade-Arenas, Inoc Mario Rubio Paucar, Cesar Yactayo-Arias |
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| DOI : 10.14445/22315381/IJETT-V72I3P108 | ||
How to Cite?
Laberiano Andrade-Arenas, Inoc Mario Rubio Paucar, Cesar Yactayo-Arias, "Expert Systems and Epidemiological Surveillance for Tuberculosis: Innovative Tools for Disease Prevention and Control," International Journal of Engineering Trends and Technology, vol. 72, no. 3, pp. 72-90, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I3P108
Abstract
Expert systems have been automating several tasks in various social spheres as the years have passed. Healthrelated fields have made it possible to accept a lot of information that is used to inform decisions and train these systems about the most dangerous diseases on a global scale. As a result, the research aims to put in place an expert online system that would allow for epidemiological surveillance of Tuberculosis. Innovative tools like web-based systems and specialized programming languages like Swi-Prolog were used to diagnose Tuberculosis. For this reason, several sources of information in other authors' research were consulted in order to learn how Tuberculosis maintains a connection with expert systems. The Buchanan methodology was used, which consists of 4 phases; finally, for the validation and acceptance of the system by the customers, a questionnaire of 15 questions separated by 3 dimensions was carried out, applying an acceptance rate of 85% of the surveyed users. Finally, the conclusion reached is that the system developed will help many people to prevent and inform them about the danger of Tuberculosis and its consequences.
Keywords
Buchanan methodology, Expert systems, Epidemiological surveillance, Health-related, Tuberculosis.
References
[1] OPS, Tuberculosis - PAHO/WHO, Pan American Health Organization, 2021. [Online]. Available: https://www.paho.org/es/temas/tuberculosis
[2] Nader Salari et al., “Global Prevalence of Drug-Resistant Tuberculosis: A Systematic Review and Meta-Analysis,” Infectious Diseases of Poverty, vol. 12, no. 1, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Maxwell Afranie Appiah et al., “Barriers to Tuberculosis Treatment Adherence in High-Burden Tuberculosis Settings in Ashanti Region, Ghana: A Qualitative Study from Patient’s Perspective,” BMC Public Health, vol. 23, no. 1, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Thara Venkatappa et al., “Association of Mycobacterium Tuberculosis Infection Test Results with Risk Factors for Tuberculosis Transmission,” Journal of Clinical Tuberculosis and Other Mycobacterial Diseases, vol. 33, pp. 1-7, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Tracy R. Richardson et al., “Field Evaluation of a Point-of-Care Triage Test for Active Tuberculosis (TriageTB),” BMC Infectious Diseases, vol. 23, no. 1, pp. 1-9, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Joshua Fieggen et al., “The Role of Machine Learning in HIV Risk Prediction,” Frontiers in Reproductive Health, vol. 4, pp. 1-8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Tajebe Tsega Mengistie, and Deepak Kumar, “Comparative Study of Transfer Learning Techniques for Lung Disease Prediction,” 10th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks, Jaipur, India, pp. 1-6, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Salma Jamal et al., “Artificial Intelligence and Machine Learning Based Prediction of Resistant and Susceptible Mutations in Mycobacterium Tuberculosis,” Scientific Reports, vol. 10, no. 1, pp. 1-16, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Reema Sharma, and Rajesh Kochher, “Fuzzy Decision Support System for Tuberculosis Detection,” Proceedings of the 2017 IEEE International Conference on Communication and Signal Processing, Chennai, India, pp. 2001-2005, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Thamyres Machado David, Teófilo Miguel de Souza, and Paloma Maria Silva Rocha Rizol, “Expert System: Use of CLIPS Software to Evaluate Solar Energy for Residences and Businesses,” Energy Informatics, vol. 6, no. 1, pp. 1-22, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Manuel Casal-Guisande et al., “Integration of the Wang & Mendel Algorithm into the Application of Fuzzy Expert Systems to Intelligent Clinical Decision Support Systems,” Mathematics, vol. 11, no. 11, pp. 1-33, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Rahul Yedida et al., “An Expert System for Redesigning Software for Cloud Applications,” Expert Systems with Applications, vol. 219, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Lilibeth Arias et al., “SMA-TB: Study Protocol for the Phase 2b Randomized Double-Blind, Placebo-Controlled Trial to Estimate the Potential Efficacy and Safety of Two Repurposed Drugs, Acetylsalicylic Acid and Ibuprofen, for Use as Adjunct Therapy Added to, and Compared with, the Standard WHO Recommended TB Regimen,” Trials, vol. 24, no. 1, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Soham Jinesh Shah, Uday E. Jadhav, and Deepi P. Agrawal, “Acquired Tracheo-Esophageal Fistula in Adulta Classical Case of ‘What Not to Do,’” Indian Journal of Thoracic and Cardiovascular Surgery, vol. 38, no. 4, pp. 442-444, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Nicholas Dwiarto Wirasbawa et al., “Expert API for Early Detection of TB Disease with Forward Chaining and Certainty Factor Algorithms,” Informatica (Slovenia), vol. 46, no. 6, pp. 117-124, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Evi Dewi Sri Mulyani et al., “Expert System for Comparative Diagnosis Probability of Lung Disease Using Nearest Neighbour Algorithm,” 3 rd International Conference on Cybernetics and Intelligent Systems, Makasar, Indonesia, pp. 1-6, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] M.Y. Santoso, A.M. Disrinama, and H.N. Amrullah, “Design of Pneumonia and Pulmonary Tuberculosis Early Detection System Based on Adaptive Neuro Fuzzy Inference System,” Journal of Physics: Conference Series, vol. 1450, no. 1, pp. 1-7, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Thi Ngoc Mai Nguyen et al., “Design of a Medical Expert System for Consulting Tuberculosis Diagnosis in Vietnam Rural Areas,” IFMBE Proceedings, vol. 69, pp. 577-583, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Khairun Nisa Meiah Ngafidin, Suryono Suryono, and R. Rizal Isnanto, “Diagnosis of Tuberculosis by using a Fuzzy Logic Expert System,” Proceedings of 2019 4th International Conference on Informatics and Computing, Semarang, Indonesia, pp. 1-6, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Dedi Leman, “Expert System Diagnose Tuberculosis Using Bayes Theorem Method and Shafer Dempster Method,” 6 th International Conference on Cyber and IT Service Management, Parapat, Indonesia, pp. 1-4, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Jimmy Josue Peña Koo, Orlando Adrian Chan May, and Cinthia del Carmen Balam Almeida, “Expert System to Support DecisionMaking for Approval of Lines of Credit,” National Technological Institute of Mexico in Celaya Educational Tracks, vol. 127, pp. 402- 411, 2017.
[Google Scholar] [Publisher Link]
[22] Linda Delgado Montenegro, Augusto Cortez Vásquez, and Esteban Ibáñez Prentice, “Application of Buchanan Methodology for the Construction of an Expert System with Bayesian Networks to Support the Diagnosis of Tetralogy of Fallot in Peru,” Industrial Data, vol. 18, no. 1, pp. 135-148, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[23] P. Latorre et al., “Freedom and Order Ministry of Social Protection Republic of Colombia Ministry of Social Protection,” Medicine and Laboratory, vol. 17, pp. 3-4, 2011.
[24] Pascual Julián-Iranzo, and Fernando Sáenz-Pérez, “BousiProlog: Design and Implementation of a Proximity-Based Fuzzy Logic Programming Language,” Expert Systems with Applications, vol. 213, 2023.
[CrossRef] [Google Scholar] [Publisher Link]