Uncovering the Most and the Least Factors Affecting Elderly Health Using Association Mining
Article Sidebar
Main Article Content
Abstract
Thailand has entered the aging society since 2005 and is now stepping towards a complete aging society in 2021. The elderly’s health problems are difficult to avoid.The research aims to analyze the most influential and most minor powerful factors on the elderly’s health status. The association analysis approach is applied to discover the relationship between various factors that affect good health. 17,804 Thai elderly data from the National Statistical Office (NSO) of Thailand were employed. One hundred ninety-nine features were taken into the association rules mining modeling. FP-growth algorithm is chosen to find the most and the least factors affecting the health of the elderly. The interesting relationships among those factors are also disclosed. As a result, the feature that demonstrates good health is performing daily activities independently. Additionally, several features that are less likely to appear in healthy seniors are hard work, working at risk, living in an urban society, living with unfamiliar caregivers, having a few children, and lacking sufficient government medical care. The knowledge gained from the findings can be considered for preparing health care for the aging society.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Mohd Tarmizi Osman, Chen Yuli, Tian Li, and Syahrul Fithry Senin. Association Rule Mining for Identification of Port State Control Patterns in Malaysian Ports. Maritime Policy & Management, 0(0):1–14, 2020.
Xi Yao, Xiaoting Pei, Yingrui Yang, Hongmei Zhang, Mengting Xia, Ranran Huang, Yuming Wang, and Zhijie Li. Distribution of Diabetic Retinopathy in Diabetes Mellitus Patients and Its Association Rules with Other Eye Diseases. Scientific Reports, 11(1):16993, 2021.
Fabian Held, David G Le Couteur, Fiona M Blyth, Vasant Hirani, Vasi Naganathan, Louise M Waite, Markus J Seibel, David J Handelsman, Robert G Cumming, Heather G Allore, and Danijela Gnjidic. Polypharmacy in Older Adults: Association Rule and Frequent-set Analysis to Evaluate Concomitant Medication Use. Pharmacological Research, 116:39 – 44, 2017. Country in focus: Pharmacology in Australia.
Shanti Verma and Jignesh Doshi. Correlation Between Text Book Usage and Academic Performance of Student in Higher Education Using R, pages 11–18. Springer, 04 2017.
Sugam Sharma. Concept of Association Rule of Data Mining Assists Mitigating the Increasing Obesity. International Journal of Information Retrieval Researchs, 7(2):1–18, apr 2017.
Yuanyuan Gao, Anqi Xu, Paul Hu, and Tsang-Hsiang Cheng. Incorporating Association Rule Networks in Feature Category-weighted Naive Bayes Model to Support Weaning Decision Making. Decision Support Systems, 96, 01 2017.
Yaofei Xie, Mengdi Ma, Wenwen Wu, Yu-peng Zhang, Yuting Zhang, and Xiaodong Tan. Factors Associated with Depressive Symptoms Among the Elderly in China: Structural Equation Model. International Psychogeriatrics, 33(2):157167, 2021.
Zhu Zhu, Dongping Zhu, Yanqing Jiang, Ying Lin, Ying Yang, and Wei Luan. Cross-sectional Study on the SF-36, the General Self-efficacy, the Social Support, and the Health Promoting Lifestyle of the Young Elderly in a Community in Shanghai, China. Annals of palliative medicine, 10(1):518529, January 2021.
Jeong Hoon Park, Sung Min, Yookyung Eoh, and Soo Hyun Park. The Elderly Living in Single-person Households in South Korea: a Latent Profile Analysis of Self-esteem, Life Satisfaction, and Depression. Quality of Life Research, 30(4):10831092, 2021.
Pravat Bhandari and Balram Paswan. Lifestyle Behaviours and Mental Health Outcomes of Elderly: Modification of Socio-economic and Physical Health Effects. Ageing International, 46(1):35 – 69, 2021.
Paul Ratanasiripong, Nop Ratanasiripong, Monpanee Khamwong, Sarinya Jingmark, Ploenpit Thaniwattananon, Pennapa Pisaipan, Ladda Sanseeha, Nongnaphat Rungnoei, Wallapa Songprakun, Asawinee Tonkuriman, and Suchart Bunyapakorn. The Impact of Resiliency on Mental Health and Quality of Life Among Older Adults in Thailand. Journal of Health Research, 2021.
Luca Cagliero and Alessandro Fiori. Discovering Generalized Association Rules from Twitter. Intelligent Data Analysis, 17:627 648, 01 2013.
Nichnan Kittiphattanabawon, Thanaruk Theeramunkong, and Ekawit Nanta jeewarawat. Region-based Association Measures for Ranking Mined News Relations. Intelligent Data Analysis, 18:217–241, 02 2014.
Grkem Saryer and Ceren cal Taar. Highlighting the Rules Between Diagnosis Types and Laboratory Diagnostic Tests for Patients of an Emergency Department: Use of Association Rule Mining. Health Informatics Journal, 26(2):1177–1193, 06 2020.
Raed Shatnawi, Qutaibah Althebyan, Baraq Ghaleb, and Mohammed Al-Maolegi. A Student Advising System Using Association Rule Mining. International Journal of Web-Based Learning and Teaching Technologies, 16(3):65–78, 2021.
National Statistical Office. The 2014 Survey of the Older Persons in Thailand. Technical report, Ministry of Information and Communication Technology, Bangkok: Text and Journal
Publication, 2014.
M. S. Mythili and A. R. Mohamed Shanavas. Performance Evaluation of Apriori and FP-growth Algorithms. International Journal of Computer Applications, 79(10):34–37, October 2013.
K. Dharmaraajan and M. A. Dorairangaswamy. Analysis of FP-growth and Apriori Algorithms on Pattern Discovery from Weblog Data. 2016 IEEE International Conference on Advances in Computer Applications (ICACA), pages 170– 174, 2016.
Rufai Yusuf and Zaharaddeen Lawal. Performance Analysis of Apriori and FP-growth Algorithms (Association Rule Mining). International Journal of Computer Applications in Technology, 7:279–293, 03 2016.
Erna Hikmawati and Kridanto Surendro. How to Determine Minimum Support in Association Rule. In Proceedings of the 2020 9th International Conference on Software and Computer Applications, ICSCA 2020, page 610, New York, NY, USA, 2020. Association for Computing Machinery.
Philippe Fournier-Viger, Cheng-Wei Wu, and Vincent S. Tseng. Mining Top-k Association Rules. In Leila Kosseim and Diana Inkpen, editors, Advances in Artificial Intelligence, pages 61–73, Berlin, Heidelberg, 2012. Springer Berlin Heidelberg.
Ratthayanaphit Phalasuek and Benjawan Thanomchayathawatch. A Family Model for Older People Care. The Southern College Net- work Journal of Nursing and Public Health, 4(3):135–150, 2017.
Patcharapong Chuanchom, Thirawat Chantuk, and Phitak Siriwong. Job Styles for Older Workers. VRU Research and Development Journal Humanities and Social Science, 13(1):107–116, 2018.
Ministry of Labour, Thailand. Notification of the Ministry of Labour: Request for Cooperation and Support for the Elderly to Have a Job, 2019. Announced on 8 March 2019.
Pramote Prasartkul, Sutthida Chuanwan, and Kanchana Thianlai. Elderly: Inner People to Be Marginalised. In Kulphawan Chansara and Krittaya Achavanitkul, editors, Population and Society 2012: Marginalized Population and Fairness in Thai Society, pages 105–124. Publication in 8th Academic Conference: Population and Society, Nakhon Pathom: Population and Social Publishing, Institute for Population and Social Research, Mahidol University, 2012.