AN INTELLIGENT POSTOPERATIVE CHRONIC PAIN PREDICTION SYSTEM (I-POCPP)

Abstract

Objective: Postoperative Chronic Pain (POCP) affects the quality of patients’ lives. Machine learning and its applications provide significant contributions to pain research. The aim of this study is to predict the POCP status of patients based on perioperative data by developing an “Intelligent POCP Prediction System (I-POCPP)” using the best performing machine learning algorithm. Material and Method: The dataset for this multi-centered study was collected from 5 tertiary hospitals in Turkey and included 733 patients who had undergone elective surgeries attended by an anesthesiologist in the operating room. Several machine learning prediction algorithms were used. POCP status of the patients diagnosed by the anesthesiologists and the prediction results of the models were compared to evaluate the performance of the models. Results: It was found that the k-Nearest Neighbour (kNN), Random Forest (RF), and C5.0 models were able to predict the POCP status of a patient with an accuracy higher than 80%. The performance of RF was considered, while the kNN algorithm has no stable model. According to RF and Classification and Regression Tree (CART) algorithms’ attribute importance ranking, “Incision site”, “Age”, and “Primary diagnosis for operation” are common attributes. Since the attribute importance ranking obtained as a result of the C5.0 algorithm was not consistent with the RF and CART models, the results of this model were not evaluated. The best result among all models was obtained by RF, and I-POCPP has been developed accordingly. Conclusion: Fast, accurate, and efficient treatment of POCP provided by I-POCPP could allow the patient to return to daily life earlier.