Designing a Data Mining-enabled Case-based System for Tuberculosis Diagnosis

Abstract Tuberculosis remains a persistent public health problem in Ethiopia, which demands innovative solutions for diagnosis and treatment support. Knowledge-based systems, particularly case-based systems, offer valuable decision-making assistance for various diseases. This paper introduces a data mining-driven approach for automatic knowledge extraction to develop a case-based system for tuberculosis diagnosis. Hidden insights are extracted from a tuberculosis dataset through the hybrid knowledge discovery process. The collected data set is pre-processed to fill in missing values, correct outliers, and remove noise. Clustering techniques, such as K-means, Make Density-based clustering, and farthest first, are employed to construct a descriptive model. The K-means algorithm is selected for its superior performance. Integration of the descriptive model into a case-based system is done using a Java-based integrator module. The prototype case-based system demonstrates an accuracy of 90%, which is a promising result. Further work is required to integrate the case-based system with rule-based towards developing a generic knowledge-based system.