Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30790
Title: An Efficient Healthcare Data Mining Approach Using Apriori Algorithm: A Case Study of Eye Disorders in Young Adults
Authors: Gulzar, Kanza 
Ayoob Memon, Muhammad 
Mohsin, Syed Muhammad 
Aslam, Sheraz 
Akber, Syed Muhammad Abrar 
Nadeem, Muhammad Asghar 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: data mining;diagnosis;health care;hospital information management system (HIMS);knowledge discovery and databases (KDD);medical data;patient record;pattern discovery
Issue Date: 1-Apr-2023
Source: Information (Switzerland), 2023, vol. 14, iss. 4
Volume: 14
Issue: 4
Journal: Information (Switzerland) 
Abstract: In the public health sector and the field of medicine, the popularity of data mining and its usage in knowledge discovery and databases (KDD) are rising. The growing popularity of data mining has discovered innovative healthcare links to support decision making. For this reason, there is a great possibility to better diagnose patient’s diseases and maintain the quality of healthcare services in hospitals. So, there is an urgent need to make disease diagnosis possible by discovering the hidden patterns from the patients’ history information in developing countries. This work is a step towards how to use the extracted knowledge to enhance the quality of healthcare facilities. In this paper, we have proposed a web-centered hospital information management system (HIMS) that identifies frequent patterns from the data with eye disorder patients using the association rule-based Apriori data mining technique. The proposed framework has the capability to overcome all the key issues and problems in the current hospital information management system regarding data analysis and reporting services. For this purpose, data were collected from more than 1000 university students (China citizens) both online and manually (printed questionnaire). After applying the Apriori algorithm on the collected data, we revealed that almost 140 individuals out of 1035 had myopia (near-sighted disorder), at current age of 22 years, and that there were no male patients found with myopia. We concluded that their clinical relevance and utility can generate favorable results from prospective clinical studies by mapping out the habits or lifestyles that potentially lead to fatal diseases. In the future, we plan to extend this work to fully automate HIMS to help practitioners to diagnose the reasons of various diseases by extracting patient lifestyle patterns.
URI: https://hdl.handle.net/20.500.14279/30790
ISSN: 2078-2489
DOI: 10.3390/info14040203
Rights: © by the authors
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Article
Affiliation : University Institute of Information Technology 
Jinnah Sindh Medical University 
COMSATS University Islamabad 
Virtual University of Pakistan 
Cyprus University of Technology 
Ctl Eurocollege 
Silesian University of Technology 
University of Sargodha 
Appears in Collections:Άρθρα/Articles

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