Utilizing Machine Learning and Feature Selection Techniques to Classify Osteoarthritis Risk Based on Consumption and Lifestyle Characteristics for the Population of Northern Thailand

Utilizing Machine Learning and Feature Selection Techniques to Classify Osteoarthritis Risk Based on Consumption and Lifestyle Characteristics for the Population of Northern Thailand
  IJETT-book-cover           
  
© 2025 by IJETT Journal
Volume-73 Issue-5
Year of Publication : 2025
Author : Ploykwan Jedeejit, Wongpanya S. Nuankaew, Pratya Nuankaew
DOI : 10.14445/22315381/IJETT-V73I5P106

How to Cite?
Ploykwan Jedeejit, Wongpanya S. Nuankaew, Pratya Nuankaew, "Utilizing Machine Learning and Feature Selection Techniques to Classify Osteoarthritis Risk Based on Consumption and Lifestyle Characteristics for the Population of Northern Thailand," International Journal of Engineering Trends and Technology, vol. 73, no. 5, pp.48-57, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I5P106

Abstract
This research has three primary objectives: to study the environment and context of people's risk of osteoarthritis in the northern region of Thailand, to use machine learning and feature selection techniques to classify osteoarthritis risk based on consumption and lifestyle characteristics for the people in the north area of Thailand and to evaluate the model of classify osteoarthritis risk based on consumption and lifestyle characteristics for the people in the northern region of Thailand. The research data is a randomly selected sample of 351 representatives from eight villages in Ko Tan Subdistrict, Khanu Woralaksaburi District, Kamphaeng Phet Province, Thailand. The research instruments consisted of three questionnaires and nine machine-learning techniques. The research results are analyzed in two parts: descriptive statistical analysis and the efficiency of the machine learning model classification. The research results found that most of the sample were engaged in agriculture, aged between 61 and 70, and had primary education. The most efficient model is the random forest technique, which is 80 percent accurate, and seven significant factors affect osteoarthritis risk prediction.

Keywords
Applied Informatics, Medical Data Mining, Medical Informatics, Medical Innovations, Screening Osteoarthritis.

References
[1] Umaporn Hanrungcharotorn et al., “Factors Influencing Physical Activity among Women with Osteoarthritis of the Knee,” Pacific Rim International Journal of Nursing Research, vol. 21, no. 1, pp. 5-17, 2017.
[Google Scholar] [Publisher Link]
[2] Ampicha Nawai et al., “Factors Associated with Nutrition Risk Among Community-Dwelling Older Adults in Thailand,” Geriatric Nursing, vol. 42, no. 5, pp. 1048-1055, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Ziad Obermeyer, and Ezekiel J. Emanuel, “Predicting the Future-Big Data, Machine Learning, and Clinical Medicine,” New England Journal of Medicine, vol. 375, no. 13, pp. 1216-1219, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Isabelle Guyon, and André Elisseeff, “An Introduction to Variable and Feature Selection,” Journal of Machine Learning Research, vol. 3, pp. 1157-1182, 2003.
[Google Scholar] [Publisher Link]
[5] Sutthichai Jitapunkul, and Suvinee Wivatvanit, “National Policies and Programs for the Aging Population in Thailand,” Ageing International, vol. 33, no. 1, pp. 62-74, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Patcharawalai Wongboonsin, Yupin Aungsuroch, and Naomi Hatsukano, The Ageing Society and Human Resources to Care for Older People in Thailand, Human Resources for the Health and Long-Term Care of Older Persons in Asia, pp. 104-135, 2020.
[Google Scholar] [Publisher Link]
[7] Pramote Prasartkul, Suchada Thaweesit, and Sutthida Chuanwan, “Prospects and Contexts of Demographic Transitions in Thailand,” Journal of Population and Social Studies, vol. 27, no. 1, pp. 1-22, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Shotaro Kumagai, “Thailand’s Efforts to Cope with a Rapidly Aging Population,” RIM Pacific Business and Industries, vol. 19, no. 71, pp. 2-28, 2019.
[Google Scholar] [Publisher Link]
[9] Srawooth Paitoonpong, and Natcha Yongphiphatwong, “Promotion of Active Aging and Quality of Life in Old Age and Preparation for a Complete Aged Society in Thailand,” TDRI Quarterly Review (September 2023), vol. 38, no. 3, pp. 1-35, 2024.
[Google Scholar] [Publisher Link]
[10] Weena Phungviwatnikul, and Jutharath Voraprateep, “Analysis of Aging Society in Thailand between A.D. 2016-2022,” Journal of Arts and Thai Studies, vol. 46, no. 1, pp. 1-13, 2024.
[Google Scholar] [Publisher Link]
[11] Prin Vathesatogkit et al., “Associations of Lifestyle Factors, Disease History and Awareness with Health-Related Quality of Life in a Thai Population,” PLOS ONE, vol. 7, no. 11, pp. 1-9, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Keerati Charoencholvanich, and Boonchana Pongcharoen, “Oxford Knee Score and SF-36: Translation & Reliability for Use with total Knee Arthroscopy Patients in Thailand,” Journal-Medical Association of Thailand, vol. 88, no. 9, pp. 1194-1202, 2005.
[Google Scholar] [Publisher Link]
[13] J.-Y. Jenny, and Y. Diesinger, “The Oxford Knee Score: Compared Performance Before and After Knee Replacement,” Orthopaedics & Traumatology: Surgery & Research, vol. 98, no. 4, pp. 409-412, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Sarah L. Whitehouse et al., “The Oxford Knee Score; Problems and Pitfalls,” The Knee, vol. 12, no. 4, pp. 287-291, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Mihaela Cazacu, and Emilia Titan, “Adapting CRISP-DM for Social Sciences,” BRAIN: Broad Research in Artificial Intelligence and Neuroscience, vol. 11, no. 2Sup1, pp. 99-106, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Fernando Martínez-Plumed et al., “CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories,” IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 8, pp. 3048-3061, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Christoph Schröer, Felix Kruse, and Jorge Marx Gómez, “A Systematic Literature Review on Applying CRISP-DM Process Model,” Procedia Computer Science, vol. 181, pp. 526-534, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Daniel, Tjeng Wawan Cenggoro, and Bens Pardamean, “A Systematic Literature Review of Machine Learning Application in COVID-19 Medical Image Classification,” Procedia Computer Science, vol. 216, pp. 749-756, 2023.
[CrossRef] [Google Scholar] [Publisher Link]