Development of a Web-Based Expert System for Early Tuberculosis Diagnosis Using K-Nearest Neighbors
DOI:
https://doi.org/10.30871/jaic.v10i2.11747Keywords:
expert system, KNN, machine learning, tuberculosis diagnosisAbstract
Tuberculosis (TB) remains a major infectious disease that continues to challenge global public health, particularly in regions with limited access to early diagnostic services. Early and accurate detection of TB is essential to prevent further transmission and ensure effective treatment. This study aims to develop an intelligent expert system for early TB diagnosis based on the K-Nearest Neighbors (KNN) algorithm. To achieve this, the proposed system classifies patient conditions as TB or Non-TB based on clinical symptoms obtained from anonymized medical record data from a regional hospital in East Java, Indonesia. The dataset comprises eight symptom-based attributes, and several experiments were conducted to optimize parameters, including test-size ratio, number of neighbors (K), and K-Fold cross-validation. Results showed that the best model performance was achieved at K = 5 with a 10% test size, yielding an F1-score of 0.8571, precision of 1.00, and recall of 0.75. In addition, functional and non-functional testing confirmed that the developed web-based system operates reliably and securely. Overall, the proposed system provides an accurate and accessible diagnostic solution for early TB detection, especially in communities with limited healthcare infrastructure.
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