Idowu P.A., Ajibola S.O., Balogun J.A., Ogunlade O.
Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria; Department of Physiological Sciences, Obafemi Awolowo University, Ile-Ife, Nigeria
Idowu, P.A., Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria; Ajibola, S.O., Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria; Balogun, J.A., Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria; Ogunlade, O., Department of Physiological Sciences, Obafemi Awolowo University, Ile-Ife, Nigeria
Cardiovascular diseases (CVD) are top killers with heart failure as one of the most leading cause of death in both developed and developing countries. In Nigeria, the inability to consistently monitor the vital signs ofpatients has led to the hospitalization and untimely death of many as a result of heartfailure. Fuzzy logic models have found relevance in healthcare services due to their ability to measure vagueness associated with uncertainty management in intelligent systems. This study aims to develop a fuzzy logic modelfor monitoring heart failure risk using risk indicators assessed from patients. Following interview with expert cardiologists, the different stages ofheartfailure was identified alongside their respective indicators. Triangular membership functions were used to fuzzify the input and output variables while the fuzzy inference engine was developed using rules elicited from cardiologists. The model was simulated using the MATLAB® Fuzzy Logic Toolbox. Copyright © 2015, IGI Global.
Cardiology; Computer circuits; Developing countries; Diseases; Fuzzy logic; Intelligent systems; Membership functions; Reconfigurable hardware; Risk assessment; Cardio-vascular disease; Healthcare services; Heart failure; Logic-based modeling; Monitoring system; Risk model; Triangular membership functions; Uncertainty management; Fuzzy inference