Short-Term Tuberculosis Incidence Rate Prediction for Europe using Machine learning Algorithms

محورهای موضوعی :

Jamilu Yahaya Maipan-uku ¹ , Nadire Cavus ² , Boran Sekeroglu ³

1 - Computer Information Systems, Near East University, N.Cyprus, Nicosia, 99138, Mersin 10, Turkey
2 - Department of Computer Science, Ibrahim Badamasi Babangida University, Lapai, Nigeria
3 - Computer Engineering, Near East University, N.Cyprus, Nicosia, 99138, Mersin 10 Turkey

تاریخ دریافت : 1402/03/20 تاریخ پذیرش : 1402/07/06 تاریخ انتشار : 1403/06/11

کلید واژه: Machine Learning, Random forest, ANN, Europe, Decision tree, Tuberculosis Incidence Rates,

چکیده مقاله :

Tuberculosis (TB) remains a significant public health concern in Europe, necessitating effective disease management and resource allocation. Predicting short-term TB incidence rates using machine learning algorithms offers a data-driven approach to aid policymakers and healthcare professionals in making informed decisions. Machine learning (ML) algorithms are essential for prediction tasks due to their ability to establish a relationship for data sequences. In this study, three machine learning algorithms, namely, Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN), are implemented to predict the tuberculosis incidence rates and to compare the efficacy of ML algorithms for tuberculosis incidence rates prediction for 2025, among Europe. Even though all models achieved considerable results, DT obtained superior prediction rates for the future TB incidence rate with MSE, MAE, and R2 of 0.000555, 0.01506, and 0.96430 while RF 0.000882, 0.01781, and 0.94329, and ANN 0.000767, 0.02315, and 0.95066. The prediction results showed that a significant decrease in TB incidence rates is expected for 2025 form 49,752 in 2019 to 38,509 in 2025, except Finland and Malta.

چکیده انگلیسی:

Tuberculosis (TB) remains a significant public health concern in Europe, necessitating effective disease management and resource allocation. Predicting short-term TB incidence rates using machine learning algorithms offers a data-driven approach to aid policymakers and healthcare professionals in making informed decisions. Machine learning (ML) algorithms are essential for prediction tasks due to their ability to establish a relationship for data sequences. In this study, three machine learning algorithms, namely, Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN), are implemented to predict the tuberculosis incidence rates and to compare the efficacy of ML algorithms for tuberculosis incidence rates prediction for 2025, among Europe. Even though all models achieved considerable results, DT obtained superior prediction rates for the future TB incidence rate with MSE, MAE, and R2 of 0.000555, 0.01506, and 0.96430 while RF 0.000882, 0.01781, and 0.94329, and ANN 0.000767, 0.02315, and 0.95066. The prediction results showed that a significant decrease in TB incidence rates is expected for 2025 form 49,752 in 2019 to 38,509 in 2025, except Finland and Malta.

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