Identifying the Most Important Factors in Determining the Osteoporosis in Women Using Data Mining Techniques
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Abstract
Osteoporosis is one of the primary causes of disability and mortality in the elderly. If osteoporosis's significant features can be identified, the risk of developing this disease will be reduced. In recent years, data mining approaches have become a suitable tool for medical researchers. This study applied data mining methods to identify osteoporosis’s significant features. This study applied data from women having osteoporosis or osteopenia in the period 2011-2019 in the Osteoporosis Diagnosis Center, Isfahan, Iran. Data mining methods such as linear regression, naïve bayes, decision tree, support vector machine, random forest, and neural network were implemented on the dataset. This study consisted of 8258 patients’ information, of which 1482 had osteoporosis. The results showed that the support vector machine, decision tree, neural network are the best method based on accuracy, precision, and AUC measures. Six candidate features were age, weight, back pain, low activity, menopause date, and previous fracture. Support vector machine, decision tree, and neural network are the best candidate techniques for predicting osteoporosis. Thin older people are more at risk of osteoporosis than other people. Yet, people with middleweight and middle age are at lower risk of osteoporosis.
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21. Genuer R, Poggi JM, Tuleau-Malot C. Variable selection using random forests. Pattern Recognit Lett 2010;31:2225-36.
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28. Alvarez SA. An exact analytical relation among recall, precision, and classification accuracy in information retrieval. Boston College, Boston, Technical Report 2002:1-22.
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30. Loeber R, Keenan K. Interaction between conduct disorder and its comorbid conditions: Effects of age and gender. Clin Psychol Rev 1994;14:497-523.
31. Shahid Z, Kalayanamitra R, McClafferty B, Kepko D, Ramgobin D, Patel R, et al. COVID‐19 and older adults: what we know. J Am Geriatr Soc 2020;68:926-9.
32. Miller MM, Allison A, Trost Z, De Ruddere L, Wheelis T, Goubert L, et al. Differential effect of patient weight on pain-related judgements about male and female chronic low back pain patients. J Pain 2018;19:57-66.
33. Zhao LJ, Liu YJ, Liu PY, Hamilton J, Recker RR, Deng HW. Relationship of obesity with osteoporosis. J Clin Endocrinol Metab 2007;92:1640-6.
34. Vuori IM. Dose–response of physical activity and low back pain, osteoarthritis, and osteoporosis. Med Sci Sports Exerc 2001;33:S551-86.
35. Chilibeck PD, Vatanparast H, Cornish SM, Abeysekara S, Charlesworth S. Evidence-based risk assessment and recommendations for physical activity: arthritis, osteoporosis, and low back pain. Appl Physiol Nutr Metab 2011;36:S49-79.
36. Watanabe R, Tanaka T, Aita K, Hagiya M, Homma T, Yokosuka K, et al. Osteoporosis is highly prevalent in Japanese males with chronic obstructive pulmonary disease and is associated with deteriorated pulmonary function. J Bone Miner Metab 2015;33:392-400.
37. Stampfer MJ, Colditz GA, Willett WC. Menopause and heart disease. Ann N Y Acad Sci 1990;592:193-203.
38. Tang MX, Jacobs D, Stern Y, Marder K, Schofield P, Gurland B, et al. Effect of oestrogen during menopause on risk and age at onset of Alzheimer's disease. Lancet. 1996;348:429-32.
2. Rachner TD, Khosla S, Hofbauer LC. Osteoporosis: now and the future. Lancet 2011;377:1276-87.
3. Sugimoto T, Sato M, Dehle FC, Brnabic AJ, Weston A, Burge R. Lifestyle-related metabolic disorders, osteoporosis, and fracture risk in Asia: A systematic review. Value Health Reg Issues 2016;9:49-56.
4. Marshall D, Johnell O, Wedel H. Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures. BMJ 1996;312:1254-9.
5. Mirzaie M, Darabi S. Population Aging in Iran and Rising Health Care Costs. Salmand 2017;12:156-69.
6. Lindsay R, Cosman F. Harrison’s Principles of Internal Medicine. In: Lindsay R; Cosman F, eds. Osteoporosis. 18th ed. United States: The McGraw Hill; 2012:3131-6.
7. International Osteoporosis Foundation (IOF). Facts and Statistics, 2015. (Accessed 2015, at www.iofbonehealth.org/facts-statistics#category-14.)
8. Center JR, Nguyen TV, Schneider D, Sambrook PN, Eisman JA. Mortality after all major types of osteoporotic fracture in men and women: an observational study. Lancet 1999;353:878-82.
9. Tapak L, Shirmohammadi-Khorram N, Amini P, Alafchi B, Hamidi O, Poorolajal J. Prediction of survival and metastasis in breast cancer patients using machine learning classifiers. Clin Epidemiol Glob Health 2019;7:293-9.
10. Moeinzadeh F, Rouhani MH, Mortazavi M, Sattari M. Prediction of chronic kidney disease in Isfahan with extracting association rules using data mining techniques. Tehran University Medical Journal TUMS Publications. 2021 Sep 10;79(6):459-67.
11. Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA. Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Comput Methods Programs Biomed 2017;141:19-26.
12. Moudani W, Shahin A, Chakik F, Rajab D. Intelligent decision support system for osteoporosis prediction. Int J Intell Inf Technol 2012;8:26-45.
13. Yoo TK, Kim SK, Kim DW, Choi JY, Lee WH, Park EC. Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning. Yonsei Med J 2013;54:1321-30.
14. Mona S, Somayeh A, Abbasi M, Ameri H. Providing a model for predicting the risk of osteoporosis using decision tree algorithms. J Mazandaran Univ Med Sci 2014;24:110-8.
15. Li H, Li X, Ramanathan M, Zhang A. Identifying informative risk features and predicting bone disease progression via deep belief networks. Methods 2014;69:257-65.
16. Guannoni N, Sassi R, Bedhiafi W, Elloumi M. A Comparison Between Classification Algorithms for Postmenopausal Osteoporosis Prediction in Tunisian Population. InInternational Conference on Information Technology in Bio-and Medical Informatics. 2016 Sep 5, Porto, Portogual: Springer, Cham, 2016:234-48
17. Pedrassani de Lira C, Toniazzo de Abreu LL, Veiga Silva AC, Mazzuchello LL, Rosa MI, Comunello E, et al. Comput Inform Nurs 2016;34:369-75.
18. Iliou T, Anagnostopoulos CN, Stephanakis IM, Anastassopoulos G. A novel data preprocessing method for boosting neural network performance: a case study in osteoporosis prediction. Inf Sci 2017;380:92-100.
19. Langarizade M, Owji L, Orooji A. Developing a decision support system for osteoporosis Prediction. J Health Adm 2019;21:87-100.
20. A Mowafy M. Osteoporosis Risk Prediction Among a Group of Postmenopausal Females: A Case-Control Study. Egypt Family Med J 2019;3:65-82.
21. Genuer R, Poggi JM, Tuleau-Malot C. Variable selection using random forests. Pattern Recognit Lett 2010;31:2225-36.
22. Friedl MA, Brodley CE. Decision tree classification of land cover from remotely sensed data. Remote Sens Environ 1997;61:399-409.
23. Montgomery DC, Peck EA, Vining GG. Introduction to linear regression analysis. New Jersey, U.S: John Wiley & Sons; 2012.
24. Rish I. An empirical study of the naive Bayes classifier. In: workshop on empirical methods in artificial intelligence. Shefield: England 2001;3:41-6.
25. Babinec T. Neural Networks and Statistical Models. Sawtooth Software Conference 1997 Nov, Sequim, WA.
26. Auria L, Moro RA. Support Vector Machines (SVM) as a Technique for Solvency Analysis. Berlin, Germany: DIW Berlin Discussion, 2009:811.
27. Mastrogiannis N, Boutsinas B, Giannikos I. A method for improving the accuracy of data mining classification algorithms. Comput Oper Res 2009;36:2829-39.
28. Alvarez SA. An exact analytical relation among recall, precision, and classification accuracy in information retrieval. Boston College, Boston, Technical Report 2002:1-22.
29. Huang J, Ling CX. Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 2005;17:299-310.
30. Loeber R, Keenan K. Interaction between conduct disorder and its comorbid conditions: Effects of age and gender. Clin Psychol Rev 1994;14:497-523.
31. Shahid Z, Kalayanamitra R, McClafferty B, Kepko D, Ramgobin D, Patel R, et al. COVID‐19 and older adults: what we know. J Am Geriatr Soc 2020;68:926-9.
32. Miller MM, Allison A, Trost Z, De Ruddere L, Wheelis T, Goubert L, et al. Differential effect of patient weight on pain-related judgements about male and female chronic low back pain patients. J Pain 2018;19:57-66.
33. Zhao LJ, Liu YJ, Liu PY, Hamilton J, Recker RR, Deng HW. Relationship of obesity with osteoporosis. J Clin Endocrinol Metab 2007;92:1640-6.
34. Vuori IM. Dose–response of physical activity and low back pain, osteoarthritis, and osteoporosis. Med Sci Sports Exerc 2001;33:S551-86.
35. Chilibeck PD, Vatanparast H, Cornish SM, Abeysekara S, Charlesworth S. Evidence-based risk assessment and recommendations for physical activity: arthritis, osteoporosis, and low back pain. Appl Physiol Nutr Metab 2011;36:S49-79.
36. Watanabe R, Tanaka T, Aita K, Hagiya M, Homma T, Yokosuka K, et al. Osteoporosis is highly prevalent in Japanese males with chronic obstructive pulmonary disease and is associated with deteriorated pulmonary function. J Bone Miner Metab 2015;33:392-400.
37. Stampfer MJ, Colditz GA, Willett WC. Menopause and heart disease. Ann N Y Acad Sci 1990;592:193-203.
38. Tang MX, Jacobs D, Stern Y, Marder K, Schofield P, Gurland B, et al. Effect of oestrogen during menopause on risk and age at onset of Alzheimer's disease. Lancet. 1996;348:429-32.
| Files | ||
| Issue | Vol 61 No 4 (2023) | |
| Section | Articles | |
| DOI | https://doi.org/10.18502/acta.v61i4.13174 | |
| Keywords | ||
| : Data mining Osteoporosis Women | ||
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How to Cite
1.
Salamat M, salamat A, Sattari M, Asgari M. Identifying the Most Important Factors in Determining the Osteoporosis in Women Using Data Mining Techniques. Acta Med Iran. 2023;61(4):229-237.
