An Efficient Healthcare Data Mining Approach Using Apriori Algorithm: A Case Study of Eye Disorders in Young Adults
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 pos...
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MDPI AG
2023-03-01
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| Online Access: | https://www.mdpi.com/2078-2489/14/4/203 |
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| author | Kanza Gulzar Muhammad Ayoob Memon Syed Muhammad Mohsin Sheraz Aslam Syed Muhammad Abrar Akber Muhammad Asghar Nadeem |
| author_facet | Kanza Gulzar Muhammad Ayoob Memon Syed Muhammad Mohsin Sheraz Aslam Syed Muhammad Abrar Akber Muhammad Asghar Nadeem |
| author_sort | Kanza Gulzar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T04:55:04Z |
| format | Article |
| id | doaj.art-56f63ba5b7674633a70a026464028511 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-11T04:55:04Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-56f63ba5b7674633a70a0264640285112023-11-17T19:44:09ZengMDPI AGInformation2078-24892023-03-0114420310.3390/info14040203An Efficient Healthcare Data Mining Approach Using Apriori Algorithm: A Case Study of Eye Disorders in Young AdultsKanza Gulzar0Muhammad Ayoob Memon1Syed Muhammad Mohsin2Sheraz Aslam3Syed Muhammad Abrar Akber4Muhammad Asghar Nadeem5University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi 46000, PakistanInternal Medicine Department, Jinnah Sindh Medical University, Karachi 75510, PakistanDepartment of Computer Science, COMSATS University Islamabad (CUI), Islamabad 45550, PakistanDepartment of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, Limassol 3036, CyprusDepartment of Computer Graphics, Vision and Digital Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, PolandDepartment of Computer Science and IT, University of Sargodha, Sargodha 40100, PakistanIn 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.https://www.mdpi.com/2078-2489/14/4/203data miningknowledge discovery and databases (KDD)hospital information management system (HIMS)diagnosispattern discoveryhealth care |
| spellingShingle | Kanza Gulzar Muhammad Ayoob Memon Syed Muhammad Mohsin Sheraz Aslam Syed Muhammad Abrar Akber Muhammad Asghar Nadeem An Efficient Healthcare Data Mining Approach Using Apriori Algorithm: A Case Study of Eye Disorders in Young Adults Information data mining knowledge discovery and databases (KDD) hospital information management system (HIMS) diagnosis pattern discovery health care |
| title | An Efficient Healthcare Data Mining Approach Using Apriori Algorithm: A Case Study of Eye Disorders in Young Adults |
| title_full | An Efficient Healthcare Data Mining Approach Using Apriori Algorithm: A Case Study of Eye Disorders in Young Adults |
| title_fullStr | An Efficient Healthcare Data Mining Approach Using Apriori Algorithm: A Case Study of Eye Disorders in Young Adults |
| title_full_unstemmed | An Efficient Healthcare Data Mining Approach Using Apriori Algorithm: A Case Study of Eye Disorders in Young Adults |
| title_short | An Efficient Healthcare Data Mining Approach Using Apriori Algorithm: A Case Study of Eye Disorders in Young Adults |
| title_sort | efficient healthcare data mining approach using apriori algorithm a case study of eye disorders in young adults |
| topic | data mining knowledge discovery and databases (KDD) hospital information management system (HIMS) diagnosis pattern discovery health care |
| url | https://www.mdpi.com/2078-2489/14/4/203 |
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