Modeling and Forecasting Tuberculosis Cases Using Machine Learning and Deep Learning Approaches: A Comparative Study

Abstract

Due to the prevalence of tuberculosis, it has become a source of concern in the world. From day to day, new cases are still discovered. To help governments and health policymakers make appropriate decisions and impose restrictions to reduce the prevalence of tuberculosis, efficient forecasting methods are required to be applied. Machine learning and deep learning techniques are commonly used due to their abilities in producing accurate results. In this paper, seven different models are applied to accomplish this objective, including SARIMAX, LSTM, CNN-LSTM Hybrid, MLP network, SVR, XGboost, and RF Regression models. These models are applied to forecast pulmonary positive, negative, and TB incidence cases. The models with the lowest errors are then chosen and used to forecast the number of pulmonary positive, negative, and TB incidence cases. The results of the experiments showed that the CNN-LSTM Hybrid and MLP networks achieved the lowest forecasting errors compared to the other models and were chosen for forecasting pulmonary negative, positive, and TB incidence cases from 2020 to 2029. The forecasting results revealed that there would be 117.861557 new pulmonary negative incidences, 153.029385 new pulmonary positive incidences, and 414.4704 new tuberculosis incidence cases.