Diagnosing Silicosis Using Adaptive Neuro-Fuzzy Inference System

Abstract Early diagnosis of disease such as Silicosis generally increases the chances for successful treatment by focusing on detecting symptomatic patients as early as possible. Early diagnosis is important for life-saving. In this study, we use an Adaptive Neuro-Fuzzy Inference System to evaluate the possibility of silica exposure leading to silicosis. The aim of this study presents Diagnosing Silicosis with ANFIS to develop proactive and predictible methods for occupational health and safety in workplace. Experimental results for the model reveal that the model successfully forecast Silicosis and an accuracy rate of 94%. Correlation Between Real Risk and Predicted Risk is detected 0,981. There is calculated the statistically significant strong and positive relationship between real outputs and ANFIS predicted outputs at the level of α = 0,05 (r=0,981;p<0,001).ANFIS provides an alternative and proactive method for diagnosing Silicosis. ANFIS can be a useful tool for occupational health and safety professionals and researchers for early diagnosis saving life.