Expert Systems in Tuberculosis Prevention Established in Certainty Factor

Tuberculosis remains a highly relevant public health concern, especially in contexts with limited access to medical services, highlighting the need for tools that support early diagnosis. In this study, a web-based expert system was developed to assist in tuberculosis detection, using Buchanan’s methodology, which consists of five phases: identification, conceptualization, formalization, implementation, and validation. The system was designed with a knowledge rule-based approach and incorporated the Certainty Factor to quantify confidence in diagnostic conclusions. Validation was carried out through expert judgment using a 15-question survey. The results showed a high overall positive consensus, with question 13 standing out as it obtained the highest mean score (4.80) and the lowest dispersion (SD = 0.61), reflecting the most favorable perception and greatest agreement among the experts. Conversely, question 4 recorded the lowest mean score (4.00) and the highest dispersion (SD = 1.12), indicating aspects of the system that generated more divided opinions. Overall, these findings confirm that the system is effective, reliable, and usable, making it a relevant tool to support clinical decision-making in resource-limited settings. As an additional contribution, the integration of complementary technologies is suggested, such as (ML) algorithms, radiological image analysis, and mobile applications for symptom tracking, in order to optimize early detection and strengthen clinical care for tuberculosis.

@article{Paucar2025,
  title     = {Expert Systems in Tuberculosis Prevention Established in Certainty Factor},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {10},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Inooc Rubio Paucar and Cesar Yactayo-Arias and Laberiano Andrade-Arenas},
  doi       = {10.14569/IJACSA.2025.01610100},
  url       = {https://doi.org/10.14569/IJACSA.2025.01610100}
}