Assessing and predicting operation variables for doctors employing industry 4.0 in health care industry using an adaptive neuro-fuzzy inference system (ANFIS) approach
Maryam Fatima, N.U.K. Sherwani, Sameen Khan, Mohd Zaheen Khan
Abstract
The chief objective of this study is to employ a predictive software called adaptive neuro-fuzzy inference system (ANFIS) approach which assess stress amongst doctors employing industry 4.0 technology during their surgeries. This study further investigates factors contributing the operation accuracy, sensitivity and specificity amongst doctors. Also, the effective performance of doctors can be optimized through earlier prediction for percentage of incorporating Industry 4.0 technologies. Survey was conducted amongst doctors using industry 4.0 technologies who provided unbiased answers to several queries in the questionnaire. The ANFIS model was employed to predict success rate of surgeries through models build with the aid of several input parameters. The outcomes such as accuracy, sensitivity and specificity were studied while employing Industry 4.0 technology which were considered significant factors influencing the perceived various kinds of surgeries in different domains. Moreover, the results of the ANFIS modelling approach showed that with increase in percentage of industry 4.0 machines in medical equipment, the operations sensitivity and accuracy increased, hence the most critical predictors. While specificity did not have any major impact on the surgeries. Henceforth, doctors can take preventive actions and simultaneously plan their work load with the aid of industry 4.0, providing better health benefits to patients making the healthcare industry much more efficient and stress-free.