Optimization of Early Detection of Tuberculosis Through Machine Learning Using Clinical Data: Development and Evaluation of Multilayer Perceptron and Extreme Learning Machine for Tuberculosis Classification

Abstract

This research takes an innovative step in the fight against Tuberculosis (TB), one of Indonesia's prominent public health challenges, by developing and evaluating Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM) models in machine learning for early detection of TB using clinical data. The main result of this study was the discovery that the MLP model, when applied without the Synthetic Minority Over-sampling Technique (SMOTE), achieved an impressive accuracy of 95.00%, signaling significant progress in TB early detection efforts. This discovery not only highlights the great potential of applying machine learning technology in improving the accuracy of TB diagnosis but also paves the way for the possible application of advanced technology in the health sector to deal with infectious diseases. This research illustrates how machine learning technology can be integrated into clinical practice to effectively detect TB cases at an early stage, thus enabling faster and more precise treatment, which can ultimately reduce the spread of the disease. This is particularly important given TB's significant impact on public health, especially in developing countries. The results also open up opportunities for further research into the application of machine learning techniques to other infectious diseases, promising a paradigm shift in the way we detect and manage various health conditions.