Unveiling the Comorbidities of Chronic Diseases in Serbia Using ML Algorithms and Kohonen Self-Organizing Maps for Personalized Healthcare Frameworks.
Nevena RankovićDragica RankovicIgor LukicNikola SavicVerica JovanovicPublished in: Journal of personalized medicine (2023)
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.
Keyphrases
- machine learning
- artificial intelligence
- neural network
- public health
- healthcare
- big data
- deep learning
- risk factors
- blood pressure
- climate change
- physical activity
- papillary thyroid
- chronic obstructive pulmonary disease
- urinary tract
- squamous cell carcinoma
- high resolution
- metabolic syndrome
- south africa
- health information
- type diabetes
- drug delivery
- adipose tissue
- air pollution
- social media
- cancer therapy
- health insurance
- case report
- skeletal muscle
- electronic health record
- subarachnoid hemorrhage
- global health
- quality improvement
- cerebral ischemia
- data analysis
- lymph node metastasis