Ensemble model for predicting chronic non-communicable diseases using Latin square extraction and fuzzy-artificial neural networks from 2013 to 2019

Nevena Rankovic; Dragica Rankovic; Igor Lukic; Nikola Savic; Verica Jovanovic

doi:10.1016/j.heliyon.2023.e22561

Ensemble model for predicting chronic non-communicable diseases using Latin square extraction and fuzzy-artificial neural networks from 2013 to 2019

Heliyon. 2023 Nov 20;9(11):e22561. doi: 10.1016/j.heliyon.2023.e22561. eCollection 2023 Nov.

Authors

Nevena Rankovic¹, Dragica Rankovic², Igor Lukic³, Nikola Savic⁴, Verica Jovanovic⁵

Affiliations

¹ Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, Warandelaan 2, Tilburg, 5037 AB, Netherlands.
² Department of Mathematics, Statistics and Informatics, Faculty of Applied Sciences, Union University "Nikola Tesla", Dusana Popovica 22, Nis, 18000, Serbia.
³ Department of Preventive Medicine, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, Kragujevac, 34000, Serbia.
⁴ Faculty of Business Valjevo, Singidunum University, Zeleznicka 5, Valjevo, 14000, Serbia.
⁵ Institute of the Public Health "Dr. Milan Jovanovic Batut", dr Subotica starijeg 5, Belgrade, 11000, Serbia.

Abstract

Background: The presented study tracks the increase or decrease in the prevalence of seventeen different chronic non-communicable diseases in Serbia. This analysis considers factors such as region, age, and gender and is based on data from two national cross-sectional studies conducted in 2013 and 2019. The research aims to accurately identify the regions with the highest percentage of affected individuals, as well as their respective age and gender groups. The ultimate goal is to facilitate organized, free preventive screenings for these population categories within a very short time-frame in the future.

Materials and methods: The study analyzed two cross-sectional studies conducted between 2013 and 2019, using data obtained from the Institute of Public Health of Serbia. Both studies involved a total of 27801 participants. The study compared the performance of Decision Tree and Support Vector Regressor models with artificial neural network (ANN) models that employed two encoding functions. The new methodology for the ANN-L36 model was based on artificial neural networks constructed using a Latin square (L36) design, incorporating Taguchi's robust design optimization.

Results: The results of the analysis from three different models have shown that cardiovascular diseases are the most prevalent illnesses among the population in Serbia, with hypertension as the leading condition in all regions, particularly among individuals aged 64 to 75 years, and more prevalent among females. In 2019, there was a decrease in the percentage of the leading disease, hypertension, compared to 2013, with a decrease from 34.0% to 32.2%. The ANN-L36 model with Fuzzy encoding function demonstrated the highest precision, achieving the smallest relative error of 0.1%.

Conclusion: To date, no studies have been conducted at the national level in Serbia to comprehensively track and identify chronic diseases in the manner proposed by this study. The model presented in this research will be implemented in practice and is set to significantly contribute to the future healthcare framework in Serbia, shaping and advancing the approach towards addressing these conditions. Furthermore, experimental evidence has shown that Taguchi's optimization approach yields the best results for identifying various chronic non-communicable diseases.

Keywords: ANN-L36-fuzzy model; Chronic non-communicable disease; Decision tree; Orthogonal vector plans; Support vector regressor.