Knowledge Discovery from Healthcare Electronic Records for Sustainable Environment

Naeem Ahmed Mahoto, Asadullah Shaikh, Mana Saleh Al Reshan, Muhammad Ali Memon and Adel Sulaiman
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Naeem Ahmed Mahoto: Department of Software Engineering, Mehran University of Engineering & Technology, Jamshoro 76062, Pakistan
Asadullah Shaikh: College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Mana Saleh Al Reshan: College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Muhammad Ali Memon: Department of Information Technology, University of Sindh, Jamshoro 76090, Pakistan
Adel Sulaiman: College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia

Sustainability, 2021, vol. 13, issue 16, 1-19

Abstract: The medical history of a patient is an essential piece of information in healthcare agencies, which keep records of patients. Due to the fact that each person may have different medical complications, healthcare data remain sparse, high-dimensional and possibly inconsistent. The knowledge discovery from such data is not easily manageable for patient behaviors. It becomes a challenge for both physicians and healthcare agencies to discover knowledge from many healthcare electronic records. Data mining, as evidenced from the existing published literature, has proven its effectiveness in transforming large data collections into meaningful information and knowledge. This paper proposes an overview of the data mining techniques used for knowledge discovery in medical records. Furthermore, based on real healthcare data, this paper also demonstrates a case study of discovering knowledge with the help of three data mining techniques: (1) association analysis; (2) sequential pattern mining; (3) clustering. Particularly, association analysis is used to extract frequent correlations among examinations done by patients with a specific disease, sequential pattern mining allows extracting frequent patterns of medical events and clustering is used to find groups of similar patients. The discovered knowledge may enrich healthcare guidelines, improve their processes and detect anomalous patients’ behavior with respect to the medical guidelines.

Keywords: knowledge discovery; data mining; sequential pattern; clustering; association analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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