Redesigning Post-Operative Processes Using Data Mining Classification Techniques

,Hayder Ghazi Alwattar

doi:10.15282/ijsecs.7.2.2021.7.0090

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.

Highlights

Data mining has become a research area with increasing importance
The results presented at the end of this section compare the implemented classification techniques using Waikato Environment for Knowledge Analysis (WEKA) software (Table 2)
Data mining technologies can be of great value to the healthcare industry

Summary

Introduction

Data mining has become a research area with increasing importance. Organizations of all sizes have started to develop and deploy data mining technologies to leverage data resources to enhance their decision making capabilities [1]. The capabilities of data analysis are a crucial factor of success for business today. The Main parts of the business world are based on IT and deal with huge amounts of electronic data. Approaches to modeling data mining in business contexts are limited in addressing a major current trend. Data Mining becomes more and more an integral part of executing a business. Tasks like placing advertisements, recommending products, or detecting fraud have become standard application yields of data mining, and have a serious implication for business products. A frequent re-engineering of business processes is a consequence of this development [2]

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Conclusion