A new hybrid risk assessment method based on Fine-Kinney and ANFIS methods for evaluation spatial risks in nursing homes

Abstract

Today, as the elderly population in the world increases, the increase in those living in nursing homes causes their problems to be even more important. Spatial hazards cause injury and death most of the time, therefore should be evaluated risks then corrective and preventive actions should be implemented. Fine-Kinney is one of the most widely used risk assessment methods, but it has some shortcomings. One of them is that risk factors such as probability, frequency, and severity are accepted as equally important, but they can have different importance weights in real-life applications. Another is that experts assess the risk magnitudes using their opinions, who usually tend to use linguistic expressions instead of crisp numbers, in incomplete information and uncertain situations. The last is that the experts' experiences are not effectively incorporated into the automation of the risk assessment. The adaptive neuro-fuzzy inference system (ANFIS) method, which is a machine learning method, can overcome all these shortcomings. In this study, a novel hybrid risk assessment method based on Fine-Kinney and ANFIS is developed to predict the class of a new occurring risk. The hybrid method was applied to nursing homes located in Turkey. The risk classes predicted with the hybrid method were compared to ones found in the traditional Fine-Kinney method. It was determined that the prediction accuracy and Fleiss kappa value of the new hybrid method were 95.745% and 0.929 respectively. Thus, the hybrid method can be used instead of the traditional Fine-Kinney method to determine the class of a new risk, because it does not require a large number of experts and provides a faster assessment.