Volume 9, Issue 2 (Apr- June 2020)
JCHR 2020, 9(2): 69-80 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
jabarpour E, Abedini A, keshtkar A. Osteoporosis Risk Prediction Using Data Mining Algorithms. JCHR 2020; 9 (2) :69-80
URL: http://jhr.ssu.ac.ir/article-1-504-en.html
URL: http://jhr.ssu.ac.ir/article-1-504-en.html
1- 1. Department of Industrial Engineering, School of Engineering, Payame Noor University, Tehran Shomal Branch, Tehran,Iran
2- 2. Department of Computer Engineering, School of Electrical and Computer, Engineering Islamic Azad University, Qazvin Branch, Iran , aminabedini.ai@gmail.com
3- 3. Department of Health Sciences Education Development, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
2- 2. Department of Computer Engineering, School of Electrical and Computer, Engineering Islamic Azad University, Qazvin Branch, Iran , aminabedini.ai@gmail.com
3- 3. Department of Health Sciences Education Development, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Abstract: (3402 Views)
Abstract
Introduction: Osteoporosis is a disease that reduces bone density and loses the quality of bone microstructure leading to an increased risk of fractures. It is one of the major causes of inability and death in elderly people. The current study aims at determining the factors influencing the incidence of osteoporosis and providing a predictive model for the disease diagnosis to increase the diagnostic speed and reduce diagnostic costs.
Methods: An Individual's data including personal information, lifestyle, and disease information were reviewed. A new model has been presented based on the Cross-Industry Standard Process CRISP methodology. Besides, Support Vector Machine (SVM) and Bayes methods (Tree Augmented Naïve Bayes (TAN)) and Clementine12 have been used as data mining tools.
Results: Some features have been detected to affect this disease. The rules have been extracted that can be used as a pattern for the prediction of the patients' status. Classification precision was calculated to be 88.39% for SVM, and 91.29% for (TAN) when the precision of TAN is higher comparing to other methods.
Conclusion: The most effective factors concerning osteoporosis are detected and can be used for a new sample with defined characteristics to predict the possibility of osteoporosis in a person.
Introduction: Osteoporosis is a disease that reduces bone density and loses the quality of bone microstructure leading to an increased risk of fractures. It is one of the major causes of inability and death in elderly people. The current study aims at determining the factors influencing the incidence of osteoporosis and providing a predictive model for the disease diagnosis to increase the diagnostic speed and reduce diagnostic costs.
Methods: An Individual's data including personal information, lifestyle, and disease information were reviewed. A new model has been presented based on the Cross-Industry Standard Process CRISP methodology. Besides, Support Vector Machine (SVM) and Bayes methods (Tree Augmented Naïve Bayes (TAN)) and Clementine12 have been used as data mining tools.
Results: Some features have been detected to affect this disease. The rules have been extracted that can be used as a pattern for the prediction of the patients' status. Classification precision was calculated to be 88.39% for SVM, and 91.29% for (TAN) when the precision of TAN is higher comparing to other methods.
Conclusion: The most effective factors concerning osteoporosis are detected and can be used for a new sample with defined characteristics to predict the possibility of osteoporosis in a person.
Review: Research |
Subject:
Health information management
Received: 2019/02/16 | Accepted: 2020/06/29 | Published: 2020/06/29
Received: 2019/02/16 | Accepted: 2020/06/29 | Published: 2020/06/29
References
1. Camacho PM, Petak SM, Binkley N, et al. American Association of Clinical Endocrinologists and American College of Endocrinology Clinical Practice guidelines for the diagnosis and treatment of postmenopausal osteoporosis-2016. Endocrine Practice. 2016; 22(4): 1-42. [DOI:10.4158/EP161435.GL]
2. Eastell, R., Treatment of postmenopausal osteoporosis. New England Journal of Medicine. 1998; 338(11): 736-746. [DOI:10.1056/NEJM199803123381107]
3. Ominsky MS, Boyce RW, Li X, et al. Effects of sclerostin antibodies in animal models of osteoporosis. Bone. 2017; 96: 63-75. [DOI:10.1016/j.bone.2016.10.019]
4. Wright VJ, Tejpar F. The New Science of Musculoskeletal Aging in Bone, Muscle, and Tendon/Ligament. InMasterful Care of the Aging Athlete. 2018: 9-15. [DOI:10.1007/978-3-319-16223-2_2]
5. Bouxsein ML, Eastell R, Lui LY, et al. Change in bone density and reduction in fracture risk: a meta-regression of published trials.Journal of Bone and Mineral Research. 2019; 34(4): 632-642. [DOI:10.1002/jbmr.3641]
6. Sugimoto T, Sato M, Dehle FC, et al. Lifestyle-related metabolic disorders, osteoporosis, and fracture risk in Asia: A systematic review. Value in Health Regional Issues. 2016; 9: 49-56. [DOI:10.1016/j.vhri.2015.09.005]
7. Johnell O, Kanis JA .An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporosis international. 2006; 17(12): 1726-1733. [DOI:10.1007/s00198-006-0172-4]
8. Kanis JA. Assessment of osteoporosis at the primary health care level. WHO Collaborating Centre for Metabolic Bone Diseases. 2007; Available at:URL: https://ci.nii.ac.jp/naid/20001202369/en/.
9. Rohollahi F. Take a look at prevalence of osteoporosis in the world and Iran. Hamshahri newspaper. Health part. 2007; 4150. Available online at: http:// www. isaarsci. ir/ general.
10. Morris JA, Kemp JP, Youlten SE, et al. An atlas of genetic influences on osteoporosis in humans and mice. Nature genetics. 2019; 51(2): 258-66. [DOI:10.1038/s41588-018-0302-x]
11. Hsu YH, Xu X, Jeong S. Genetic Determinants and Pharmacogenetics of Osteoporosis and Osteoporotic Fracture. InOsteoporosis. 2020: 485-506. [DOI:10.1007/978-3-319-69287-6_25]
12. Pisani P, Renna MD, Conversano F, et al. Major osteoporotic fragility fractures: Risk factor updates and societal impact. World Journal of Orthopedics. 2016; 7(3): 171. [DOI:10.5312/wjo.v7.i3.171]
13. Unnikrishnan G, Xu C, Popp KL, et al. Regional variation of bone density, microarchitectural parameters, and elastic moduli in the ultradistal tibia of young black and white men and women. Bone. 2018; 112: 194-201. [DOI:10.1016/j.bone.2018.05.004]
14. Bitar AN, Sulaiman SA, Ali IA, et al. Osteoporosis among patients with chronic obstructive pulmonary disease: Systematic review and meta-analysis of prevalence, severity, and therapeutic outcomes. Journal of Pharmacy & Bioallied Sciences. 2019; 11(4): 310-320. [DOI:10.4103/jpbs.JPBS_126_19]
15. Vidal M, Thibodaux RJ, Neira LF, et al. Osteoporosis: a clinical and pharmacological update. Clinical Rheumatology. 2019; 38(2): 385-395. [DOI:10.1007/s10067-018-4370-1]
16. Sugimoto T, Fujiwara S, Hagino H, et al. Clinical practice guide on fracture risk in lifestyle-related diseases. 2011.
17. Lindsay R, Cosman F. Harrison's Principles of Internal Medicine: Osteoporosis. 2012: 3131-3136.
18. Wilson DJ. Osteoporosis and sport. European journal of radiology. 2019; 110:169-74. [DOI:10.1016/j.ejrad.2018.11.010]
19. Saadi HF, Reed RL, Carter AO, et al. Bone density estimates and risk factors for osteoporosis in young women. Eastern Mediterranean Health Journal. 2001; 7 (4-5): 730-737.
20. Mäkitie R, Costantini A, Kämpe A,et al. New insights into monogenic causes of osteoporosis. Frontiers in Endocrinology.2019; 10: 70. [DOI:10.3389/fendo.2019.00070]
21. Rizzoli R, Bonjour JP, Ferrari SL. Osteoporosis, genetics and hormones. Journal of Molecular Endocrinology. 2001; 26(2): 79-94. [DOI:10.1677/jme.0.0260079]
22. Kaewboonchoo O, Sung FC, Lin CL, et al. Risk of osteoporosis and fracture in victims with burn injury. Osteoporosis International. 2019; 30(4): 837-843. [DOI:10.1007/s00198-018-04818-2]
23. Bass S, Pearce G, Bradney M, et al. Exercise before puberty may confer residual benefits in bone density in adulthood: studies in active prepubertal and retired female gymnasts. Journal of Bone and Mineral Research. 1998; 13(3): 500-507. [DOI:10.1359/jbmr.1998.13.3.500]
24. Lee M, Jung R, Jung Y, et al. Association of Work-Time, Leisure-Time Physical Activity and Osteoporosis Prevalence: Korea National Health and Nutrition Examination Survey in 2015-2016. Korean Journal of Family Practice. 2019; 9(5): 403-407. [DOI:10.21215/kjfp.2019.9.5.403]
25. Fredman L, Ranker LR, Strunin L, et al. Caregiving Intensity and Mortality in Older Women, Accounting for Time-Varying and Lagged Caregiver Status: The Caregiver-Study of Osteoporotic Fractures Study. The Gerontologist. 2019; 59(5): 461-469. [DOI:10.1093/geront/gny135]
26. Sambrook P, Dequiker J, Rasp H. metabolic bone disease, Report of a WHO study group, Assessment of fracture risk and its application to screening for postmenopausal Osteoporosis. World Health Organization Technical Report Series. Geneva. 1994; 5.
27. Wang W, Richards G, Rea S. Hybrid Data Mining Ensemble for Predicting Osteoporosis Risk. 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai. 2005: 886-889.
28. Gao Z, Hong W, Xu Y, et al. Osteoporosis Diagnosis Based on the Multifractal Spectrum Features of Micro-CT Images and C4. 5 Decision Tree. In 2010 First International Conference on Pervasive Computing, Signal Processing and Applications. 2010: 1043-1047. [DOI:10.1109/PCSPA.2010.257]
29. Moudani W, Shahin A, Chakik F, et al. Intelligent predictive osteoporosis system. International Journal of Computer Applications. 2011; 32(5):28-37.
30. Mona S, Somayeh A, Abbasi M, et al. Providing a model for predicting the risk of osteoporosis using decision tree algorithms. Journal of Mazandaran University of Medical Sciences. 2014; 24(116): 110-118.
31. Fitzpatrick, L. A. (2002, May). Secondary causes of osteoporosis. In Mayo Clinic Proceedings. 2002 ;77(5): 453-468 Elsevier. [DOI:10.4065/77.5.453]
32. Bogoch ER, Elliot-Gibson V, Wang RY, et al. Secondary causes of osteoporosis in fracture patients. Journal of Orthopaedic Trauma. 2012; 26(9): 145-152. [DOI:10.1097/BOT.0b013e3182323f2c]
33. Raisz LG. Screening for osteoporosis. New England Journal of Medicine. 2005; 353(2): 164-171. [DOI:10.1056/NEJMcp042092]
34. Zanker J, Duque G. Osteoporosis in older persons: old and new players. Journal of the American Geriatrics Society. 2019; 67(4): 831-840. [DOI:10.1111/jgs.15716]
35. Dubey R, Samantaray SR, Panigrahi BK, et al. Data-mining model based adaptive protection scheme to enhance distance relay performance during power swing. International Journal of Electrical Power & Energy Systems. 2016; 81: 361-370. [DOI:10.1016/j.ijepes.2016.02.014]
36. Xu N, Wang Y, Xu Y, et al. Effect of subclinical hyperthyroidism on osteoporosis: A meta-analysis of cohort studies. Endocrine. 2020. [DOI:10.1007/s12020-020-02259-8]
37. Elkady AA, Kazem HH, Elgendy EA. Protective effect of vitamin D against rats' mandibular osteoporosis induced by corticosteroids and gamma rays. International Journal of Radiation Research. 2020; 18(1): 125-131.
38. Mullin BH, Tickner J, Zhu K, et al. Characterisation of genetic regulatory effects for osteoporosis risk variants in human osteoclasts. Genome biology. 2020; 21(1): 1-3. [DOI:10.1186/s13059-020-01997-2]
39. Ireland N, Nabi S, Chand K, et al. Menopause and osteoporosis. Pulse. 2019; 50.
40. Fatahi M. Suport Vector Machines: A survey [dissertation]. [razi]: razi university: 2015: 48.
41. Huang S, Cai N, Pacheco PP, et al. Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics-Proteomics. 2018; 15(1): 41-51. [DOI:10.21873/cgp.20063]
42. Koski T, Noble J. Bayesian networks: an introduction. 2011; 924. John Wiley & Sons.
43. Larijani B. An overview of osteoporosis in Iran. international osteoporosis seminar in Iran. 2004;1
44. Dreiher J, Weitzman D, Cohen AD. Psoriasis and osteoporosis: a sex-specific association?. Journal of Investigative Dermatology. 2009; 129(7): 1643-9. [DOI:10.1038/jid.2008.432]
45. Englebardt SP, Nelson R. Health care informatics: An interdisciplinary approach. Mosby Incorporated. 2002.
46. Zhou XH, Li SL, Tian F, et al. Building a disease risk model of osteoporosis based on traditional Chinese medicine symptoms and western medicine risk factors. Statistics in medicine. 2012; 31(7): 643-52. [DOI:10.1002/sim.4382]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
