Redesigning Post-Operative Processes Using Data Mining Classification Techniques

Abstract

Data mining classification models are developed and investigated in this paper. These models are adopted to develop and redesign several business processes based on post-operative data. Post-operative data were collected and used via the Waikato Environment for Knowledge Analysis (WEKA), to investigate the factors influencing patients’ admission after surgery and compare the developed DM classification models. The results reveal that each implemented DM technique entails different attributes affecting patients’ post-surgery admission status. The comparison suggests that neural networks outperform other classification techniques. Further, the optimal number of beds required to accommodate post-operative patients is investigated. The simulation was conducted using queuing theory software to compute the expected number of beds required to achieve zero waiting time. The results indicate that the number of beds required to accommodate post-surgery patients waiting in the queue is the length of 1, which means that one bed will be available due to patient discharge.