A New Risk Calculation Method for Tuberculosis (TB) based on Decision Tree and Artificial Neural Network

Tuberculosis is a bacterial infection that affects the respiratory system and may lead to the death of lung cells. In this study, a novel equation based on Decision Tree and Artificial Neural Network application was developed to calculate the risk percentage of tuberculosis. Retrospective data was input in a conventional equation based on the classification of tuberculosis risk factors. Existing methods for disease risk calculation can only predict the odds of contracting the disease, and some are based on population. Therefore, in this study, a new risk prediction method is proposed, where the risk percentage can be calculated as long as the risk factors are known. The risk factors are categorized into three groups, which are Low-, Middle-, and High-energy levels. The calculation is based on a Decision Tree and Artificial Neural Network. Artificial Neural Network was applied for output verification of the novel conventional equation. The limitation of this study is that only common risk factors have been considered. It is recommended that future studies also include other risk factors for better and more precise results. Based on the results of this study, it can be concluded that the proposed method is better than other methods because it takes into account disease risk factors.