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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2103/1/jods2024_65.pdf http://eprints.intimal.edu.my/2103/2/641 http://eprints.intimal.edu.my/2103/ http://ipublishing.intimal.edu.my/jods.html |
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| Summary: | 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. |
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