Bingol University, Turkey
Title: Applications of fuzzy logic in healthcare under uncertainty for decision support
Biography: Imran Aslan
Fuzzy logic can quantify and reason linguistic expression having ambiguous meaning. Fuzzy logic can handle this imprecision and uncertainty. This study aims to explain how fuzzy logic can handle that and improve decision making in healthcare. The past studies and current applications of fuzzy logic are chosen as the method of study, as a kind of review and creating future studies. Information about the patient, medical history of patients, physical examinations, results of laboratory tests and results of histological are critical information for accurate treatment of patients. Fuzzy logic has been applied in almost all fields of healthcare such as internal medicine, anesthesia, radiology, electrophysiology, pharmacokinetics etc. for modeling and control of data. Also, remote monitoring systems with a remote monitoring center to take action in the case of distress situation can be used to collect data of patients with insufficient cardiac heart, asthma, diabetes or Alzheimer’s disease etc. through defining walking, running, standing up, setting down, laying, sleeping, cleaning, bathing and exercising daily activities of patients. An eventual short delay, afterwards or pushed the call button in emergency cases can be known by wearable sensors connected to a remote computer as determining heart rate. Complex data acquired from the different sensors can be handled by fuzzy logic to determine human activities in order to provide support for safety, comfort, and convenience through sound classification labeled on a numerical scale. Improving care for elderly people and reducing the healthcare cost can be the main outputs of these fuzzy logic supported systems. Determining the type of disease, or diseased patients, the risk ratio of a disease by a data mining algorithm in constructing a decision support system is the fuzzy logic application in healthcare decision-making. Fuzzy IF-THEN rules are used to estimate the current situation of patient. “If the back pain is severe and the patient is old, then apply acupuncture to certain point for a long time” can be determined by a fuzzy logic application rather than going to a doctor and more than programming skills and true-false statements is required in that case. Bayesian Network (BN), Artificial Neural Network (ANN), Fuzzy Inference System, Genetic Algorithms (GAs), Swarm Intelligence, and Fuzzy Cognitive Maps (FCMs) methods can be used to make more precious choices. Furthermore, service activities are categorized by fuzzy extended AHP (Analytic Hierarchy Process) model that tangibles, reliability, responsiveness, assurance and empathy are priority of service dimensions. Identification activities for building a model for measuring the home anxiety is expected to be measured by these holistic systems as future development scientifically and technologically. Identifying the cancer risk, heart attack risk, kidney disease, birth defects, diabetics’ potential, risk of living after a surgery etc. are future area of fuzzy logic combination of medicine and engineering experts based on current and past health status of a patient.