Data-Driven Expert System for Tuberculosis (TB) Diagnosis Using the Forward Chaining Method
Tuberculosis (TBC) is a disease caused by Mycobacterium tuberculosis, one of the oldest known diseases affecting humans. While it primarily affects the lungs, about one-third of cases involve other organs, underscoring the importance of early detection and accurate diagnosis. To address this, a d...
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| Format: | Article |
| Language: | English English |
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INTI International University
2024
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| Online Access: | http://eprints.intimal.edu.my/2103/ http://eprints.intimal.edu.my/2103/1/jods2024_65.pdf http://eprints.intimal.edu.my/2103/2/641 |
| _version_ | 1848766920915943424 |
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| author | Budi, Usmanto Rinawati, . Novita, Andriyani |
| author_facet | Budi, Usmanto Rinawati, . Novita, Andriyani |
| author_sort | Budi, Usmanto |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Tuberculosis (TBC) is a disease caused by Mycobacterium tuberculosis, one of the oldest
known diseases affecting humans. While it primarily affects the lungs, about one-third of cases
involve other organs, underscoring the importance of early detection and accurate diagnosis. To
address this, a data-driven expert system has been developed to assist in diagnosing tuberculosis
and providing relevant information to users. An expert system is a form of intelligent software
that leverages data and expert knowledge to solve complex problems. In this study, the Forward
Chaining method is applied, utilizing a rule-based approach to process data and conclusions
from known facts. This method iteratively matches facts to rules, deriving new insights until a
conclusion is reached or no further matches are found. If the premise satisfies the conditions
(evaluated as TRUE), the system generates a decision. The system is designed to simplify the
recognition of tuberculosis symptoms by analyzing user-provided data to produce accurate
diagnostic results and actionable solutions. Findings indicate that the data-driven approach
enhances the system's ability to provide precise diagnoses and recommendations, ensuring
reliability and effectiveness. This work demonstrates the value of integrating data-driven
methodologies in expert systems to improve healthcare delivery, particularly in the early
detection and management of tuberculosis. |
| first_indexed | 2025-11-14T11:58:49Z |
| format | Article |
| id | intimal-2103 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:49Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-21032024-12-26T06:35:22Z http://eprints.intimal.edu.my/2103/ Data-Driven Expert System for Tuberculosis (TB) Diagnosis Using the Forward Chaining Method Budi, Usmanto Rinawati, . Novita, Andriyani QA Mathematics QA75 Electronic computers. Computer science R Medicine (General) Tuberculosis (TBC) is a disease caused by Mycobacterium tuberculosis, one of the oldest known diseases affecting humans. While it primarily affects the lungs, about one-third of cases involve other organs, underscoring the importance of early detection and accurate diagnosis. To address this, a data-driven expert system has been developed to assist in diagnosing tuberculosis and providing relevant information to users. An expert system is a form of intelligent software that leverages data and expert knowledge to solve complex problems. In this study, the Forward Chaining method is applied, utilizing a rule-based approach to process data and conclusions from known facts. This method iteratively matches facts to rules, deriving new insights until a conclusion is reached or no further matches are found. If the premise satisfies the conditions (evaluated as TRUE), the system generates a decision. The system is designed to simplify the recognition of tuberculosis symptoms by analyzing user-provided data to produce accurate diagnostic results and actionable solutions. Findings indicate that the data-driven approach enhances the system's ability to provide precise diagnoses and recommendations, ensuring reliability and effectiveness. This work demonstrates the value of integrating data-driven methodologies in expert systems to improve healthcare delivery, particularly in the early detection and management of tuberculosis. INTI International University 2024-12 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2103/1/jods2024_65.pdf text en cc_by_4 http://eprints.intimal.edu.my/2103/2/641 Budi, Usmanto and Rinawati, . and Novita, Andriyani (2024) Data-Driven Expert System for Tuberculosis (TB) Diagnosis Using the Forward Chaining Method. Journal of Data Science, 2024 (65). pp. 1-10. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA Mathematics QA75 Electronic computers. Computer science R Medicine (General) Budi, Usmanto Rinawati, . Novita, Andriyani Data-Driven Expert System for Tuberculosis (TB) Diagnosis Using the Forward Chaining Method |
| title | Data-Driven Expert System for Tuberculosis (TB) Diagnosis Using the Forward Chaining Method |
| title_full | Data-Driven Expert System for Tuberculosis (TB) Diagnosis Using the Forward Chaining Method |
| title_fullStr | Data-Driven Expert System for Tuberculosis (TB) Diagnosis Using the Forward Chaining Method |
| title_full_unstemmed | Data-Driven Expert System for Tuberculosis (TB) Diagnosis Using the Forward Chaining Method |
| title_short | Data-Driven Expert System for Tuberculosis (TB) Diagnosis Using the Forward Chaining Method |
| title_sort | data-driven expert system for tuberculosis (tb) diagnosis using the forward chaining method |
| topic | QA Mathematics QA75 Electronic computers. Computer science R Medicine (General) |
| url | http://eprints.intimal.edu.my/2103/ http://eprints.intimal.edu.my/2103/ http://eprints.intimal.edu.my/2103/1/jods2024_65.pdf http://eprints.intimal.edu.my/2103/2/641 |