Communications of the ACM
Automated extraction of decision rules for leptin dynamics-A rough sets approach
Journal of Biomedical Informatics
Approximation algorithms for combinatorial problems
Journal of Computer and System Sciences
A primary estimation of the cardiometabolic risk by using artificial neural networks
Computers in Biology and Medicine
Predicting body fat percentage based on gender, age and BMI by using artificial neural networks
Computer Methods and Programs in Biomedicine
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This paper aims to investigate possible application of the rough set approach to table-organized data in the medical domain, which reveals some relationships among sagittal abdominal diameter, anthropometric parameters and cardiovascular risk factors. When applied to table-organized data, the methodology based on the rough set theory is capable of producing decision rules in the form of If-Then rules. Such rules are suitable for inspection, examination and further analysis. By examination of the selected 30 decision rules, sagittal abdominal diameter could point out a group of obese and preobese patients with high content of visceral fat with different combination and composition of cardiovascular risk factors. These results suggest that sagittal abdominal diameter could be a clinically useful marker for identification of risk factors, combination and structure of total cardiovascular risk by applying different rules in obese and preobese persons.