Ontology-Based Reasoning to Classify Behaviors Associated with Chronic Disease Risk Factors
Context: The main chronic diseases are heart disease, cancers, chronic respiratory diseases, and diabetes, and they are among the leading causes of death worldwide. Problem: Risk factors related to chronic diseases are correlated with people's lifestyles, and early changes can prevent many chronic disease deaths. Solution: This article proposes an ontology called B-Track Onto that classifies behaviors that attenuate or worsen the risk factors associated with chronic diseases. This ontology works with user behavior profiles and makes recommendations for healthier behaviors. B-Track Onto serves as a knowledge model for information systems that aim to track behaviors associated with risk factors for chronic diseases. SI Theory: The Behavioral Decision Theory was approached, mainly in the incorporation of real patterns of decision making. Method: The ontology has axioms and semantic rules used to provide queries and inferences about its instantiated base. The definition of seven questions allowed inferences to evaluate the ontology. Summary of Results: The MIMIC-III dataset was used as base to import 21 patients from the clinical samples. B-Track Onto inferred all imported patients and categorized them in the expected classes. Besides, this work executed SPARQL queries to answer the competence questions, which returned the expected results for each question. Contributions and Impact in the IS area: B-Track Onto is the first ontology to correlate human behavior and risk factors of chronic diseases, being a potential tool for classifying preventive and non-preventive behaviors. A specialist can use these classification results as part of an Information System in a decision support platform.
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