Multiobjective Optimization of Fuzzy System for Cardiovascular Risk Classification

• Published in: Computation Vol. 11; no. 7; p. 147
• Main Authors: Villamil, Hanna C, Espitia, Helbert E, Bejarano, Lilian A
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2023
• Subjects: Adipose tissue; Artificial intelligence; Blood pressure; Body mass index; Body size; Cardiovascular diseases; Casualties; Cholesterol; Classification; Decision making; Fuzzy algorithms; Fuzzy logic; Fuzzy sets; fuzzy system; Fuzzy systems; Genetic algorithms; healthcare; Mathematical optimization; Mortality; multiobjective optimization; Multiple objective analysis; Obesity; overweight; Pareto optimization; Risk factors; Triglycerides; Colombia
• ISSN: 2079-3197; 2079-3197
• DOI: 10.3390/computation11070147

Since cardiovascular diseases (CVDs) pose a critical global concern, identifying associated risk factors remains a pivotal research focus. This study aims to propose and optimize a fuzzy system for cardiovascular risk (CVR) classification using a multiobjective approach, addressing computational aspects such as the configuration of the fuzzy system, the optimization process, the selection of a suitable solution from the optimal Pareto front, and the interpretability of the fuzzy logic system after the optimization process. The proposed system utilizes data, including age, weight, height, gender, and systolic blood pressure to determine cardiovascular risk. The fuzzy model is based on preliminary information from the literature; therefore, to adjust the fuzzy logic system using a multiobjective approach, the body mass index (BMI) is considered as an additional output as data are available for this index, and body mass index is acknowledged as a proxy for cardiovascular risk given the propensity for these diseases attributed to surplus adipose tissue, which can elevate blood pressure, cholesterol, and triglyceride levels, leading to arterial and cardiac damage. By employing a multiobjective approach, the study aims to obtain a balance between the two outputs corresponding to cardiovascular risk classification and body mass index. For the multiobjective optimization, a set of experiments is proposed that render an optimal Pareto front, as a result, to later determine the appropriate solution. The results show an adequate optimization of the fuzzy logic system, allowing the interpretability of the fuzzy sets after carrying out the optimization process. In this way, this paper contributes to the advancement of the use of computational techniques in the medical domain.