Affiliation:
1. Department of Cognitive Science and Artificial Intelligence, School of Humanities and Digital Sciences, Tilburg University, 5037 AB Tilburg, The Netherlands
2. Department of Mathematics, Informatics and Statistics, Faculty of Applied Sciences, Union University “Nikola Tesla”, 18 000 Nis, Serbia
3. Department of Preventive Medicine, Faculty of Medical Sciences, University of Kragujevac, 34 000 Kragujevac, Serbia
4. Department of Healthcare, Faculty of Business Valjevo, Singidunum University, 14 000 Valjevo, Serbia
5. Institute of the Public Health “Dr. Milan Jovanovic Batut”, 11 000 Belgrade, Serbia
Abstract
In previous years, significant attempts have been made to enhance computer-aided diagnosis and prediction applications. This paper presents the results obtained using different machine learning (ML) algorithms and a special type of a neural network map to uncover previously unknown comorbidities associated with chronic diseases, allowing for fast, accurate, and precise predictions. Furthermore, we are presenting a comparative study on different artificial intelligence (AI) tools like the Kohonen self-organizing map (SOM) neural network, random forest, and decision tree for predicting 17 different chronic non-communicable diseases such as asthma, chronic lung diseases, myocardial infarction, coronary heart disease, hypertension, stroke, arthrosis, lower back diseases, cervical spine diseases, diabetes mellitus, allergies, liver cirrhosis, urinary tract diseases, kidney diseases, depression, high cholesterol, and cancer. The research was developed as an observational cross-sectional study through the support of the European Union project, with the data collected from the largest Institute of Public Health “Dr. Milan Jovanovic Batut” in Serbia. The study found that hypertension is the most prevalent disease in Sumadija and western Serbia region, affecting 9.8% of the population, and it is particularly prominent in the age group of 65 to 74 years, with a prevalence rate of 33.2%. The use of Random Forest algorithms can also aid in identifying comorbidities associated with hypertension, with the highest number of comorbidities established as 11. These findings highlight the potential for ML algorithms to provide accurate and personalized diagnoses, identify risk factors and interventions, and ultimately improve patient outcomes while reducing healthcare costs. Moreover, they will be utilized to develop targeted public health interventions and policies for future healthcare frameworks to reduce the burden of chronic diseases in Serbia.