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Article:Intelligent Predictive Osteoporosis System

by Walid MOUDANI, Ahmad SHAHIN, Fadi CHAKIK, Dima RAJAB
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 32 - Number 5
Year of Publication: 2011
Authors: Walid MOUDANI, Ahmad SHAHIN, Fadi CHAKIK, Dima RAJAB
10.5120/3901-5468

Walid MOUDANI, Ahmad SHAHIN, Fadi CHAKIK, Dima RAJAB . Article:Intelligent Predictive Osteoporosis System. International Journal of Computer Applications. 32, 5 ( October 2011), 28-37. DOI=10.5120/3901-5468

@article{ 10.5120/3901-5468,
author = { Walid MOUDANI, Ahmad SHAHIN, Fadi CHAKIK, Dima RAJAB },
title = { Article:Intelligent Predictive Osteoporosis System },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 32 },
number = { 5 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 28-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume32/number5/3901-5468/ },
doi = { 10.5120/3901-5468 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:18:24.100073+05:30
%A Walid MOUDANI
%A Ahmad SHAHIN
%A Fadi CHAKIK
%A Dima RAJAB
%T Article:Intelligent Predictive Osteoporosis System
%J International Journal of Computer Applications
%@ 0975-8887
%V 32
%N 5
%P 28-37
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The healthcare environment is generally perceived as being information rich yet knowledge poor. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. The information technology may provide alternative approaches to Osteoporosis disease diagnosis. In this study, we examine the potential use of classification techniques on a massive volume of healthcare data, particularly in prediction of patients that may have Osteoporosis Disease (OD) through its risk factors. For this purpose, we propose to develop a new solution approach based on Random Forest (RF) decision tree to identify the osteoporosis cases. There has been no research in using the afore-mentioned algorithm for Osteoporosis patients’ prediction. The reduction of the attributes consists to enumerate dynamically the optimal subsets of the reduced attributes of high interest by reducing the degree of complexity. A computer-aided system is developed for this purpose. The study population consisted of 2845 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

References
  1. Taylor BC, Schreiner PJ, Stone KL, et al., 2004. Long-term prediction of incident hip fracture risk in elderly white women: study of osteoporotic fractures. J Am Geriatr Soc. 2004;52:1479–1486.
  2. PubMed
  3. Kanis JA. 2002. Diagnosis of osteoporosis and assessment of fracture risk. Lancet. 2002;359:1929–1936.
  4. PubMed
  5. Kanis JA, Johnell O, Oden A, Johansson H, McCloskey E. 2008. FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int. 2008;19:385–397.
  6. PMC free article
  7. PubMed
  8. Kanis JA, Johansson H, Johnell O, et al., 2005. Alcohol intake as a risk factor for fracture. Osteoporos Int.2005;16:737–742.
  9. PubMed
  10. De Laet C, Kanis JA, Oden A, et al. 2005. Body mass index as a predictor of fracture risk: a meta-analysis. Osteoporos Int. 2005;16:1330–1338.
  11. PubMed
  12. Kanis JA, Johnell O, Oden A, et al., 2005. Smoking and fracture risk: a meta-analysis. Osteoporos Int.2005;16:155–162.
  13. PubMed
  14. Kanis JA, Oden A, Johnell O, et al., 2007. The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women. Osteoporos Int.2007;18:1033–1046.
  15. PubMed
  16. FRAX® WHO fracture risk assessment tool
  17. 8-13-2008. http://www.shef.ac.uk/FRAX/
  18. Dawson-Hughes B, Tosteson AN, Melton LJ, III, et al., 2008. Implications of absolute fracture risk assessment for osteoporosis practice guidelines in the USA. Osteoporos Int. 2008;19:449–458.
  19. PubMed
  20. Leo Brieman. Random Forests. In: Machine Learning, Kluwer Academic Publisher, 45(1), 2001
  21. K. Thangavel, Q. Shen, and A. Pethalakshmi, 2006. “Application of Clustering for Feature selection based on rough set theory approach”, AIML Journal, Vol. 6 (1), pp.19-27.
  22. Moudani W., Shahin A., Chakik F., and Mora-Camino, F., 2011. Dynamic Rough Sets Features Reduction, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9(4).
  23. R.E. Bellman, 1957. Dynamic Programming, Princeton University Press.
  24. F. Berzal, J.C. Cubero, N. Marın, D. Sanchez, Building multi-way decision trees with numerical attributes, Technical report available upon request, 2001.
  25. L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone, Classification and Regression Trees, Wadsworth, California, USA, 1984, ISBN 0-534-98054-6.
  26. R. Lopez de Mantaras, A distance-based attribute selection measure for decision tree induction, Mach. Learn. 6 (1991).
  27. J.R. Quinlan, Learning decision tree classifiers, ACM Comput. Surveys 28 (1) (1986) 71–72.
  28. J.R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993, ISBN 1-55860-238-0.
  29. J.R. Quinlan, Improved use of continuous attributes in C4.5, J. Artif. Intell. Res. 4 (1996) 77– 90.
  30. P.C. Taylor, B.W. Silverman, Block diagrams and splitting criteria for classification trees, Statist. Comput. 3 (4) (1993) 147–161.
  31. Adinoff AD & Hollister JR. Steroid induced fractures and bone loss in patients with asthma. N. Engl. J. Med. 1983; 309: 265–8.
  32. Melton LJ 3rd, Kan SH, Wahner HW, Riggs BL (1988) Lifetime fracture risk: an approach to hip fracture risk assessment based on bone mineral density and age. J Clin Epidemiol 41:985–994
  33. Koh LK, Sendrine WB, Torralba TP, et al. A simple tool to identify asian women at increased risk of osteoporosis. Osteoporos Int. 2001;12(8):699-705.
  34. Sen SS, Rives VP, Messina OD, et al. A risk assessment tool (OsteoRisk) for identifying Latin American women with osteoporosis. J Gen Intern Med. 2005;20(3):245-50.
  35. Chiu JS, Li YC, Yu FC, Wang YF, Applying an artificial neural network to predict osteoporosis in the elderly. Osteoporos Int. 2006;124:609-14.
  36. PubMed
  37. Wang W. and Rea S. Intelligent ensemble system aids osteoporosis early detection. EC'05 Proceedings of the 6th World Scientific and Engineering Academy and Society (WSEAS) international conference on Evolutionary computing, 2005.
  38. Yildirim P., Çeken Ç, Hassanpour R., Esmelioglu S. and Tolun M.R. Mining MEDLINE for the Treatment of Osteoporosis, JOURNAL OF MEDICAL SYSTEMS, DOI: 10.1007/s10916-011-9701-6, April 2011.
  39. Schapire et al (1997): Boosting the margin: A new explanation for the effectiveness of voting methods. In Fisher, D.(Ed) Machine Learning: Proceedings of 14th Int. Conference, Morgan Kaufmann.
  40. Freund, Y. & Schapire R.E. (1996): Experiments with a new boosting algorithm, in L. Saitta, ed., Machine Learning: Proceedings of the 13th national conference, Morgan Kaufmann. pp148-156
  41. A. Hedar, J. Wangy, and M. Fukushima, “Tabu search for attribute reduction in rough set theory”, Journal of Soft Computing - A Fusion of Foundations, Methodologies and Applications, Springer-Verlag Berlin, Heidelberg, 2008, Vol. 12 (9).
  42. F. Glover and M. Laguna, “Tabu Search”, Kluwer Academic Publishers, Boston, MA, USA, 1997.
  43. A. Hedar and M. Fukushima, “Tabu search directed by direct search methods for nonlinear global optimization”, European Journal of Operational Research, 2006, Vol. 170, pp. 329–349.
  44. Leo Breiman. Bagging predictors. Machine Learning Journal, 26(2):123140, 1996.
  45. T.K. Ho. 1998. The random subspace method for constructing decision forests. IEEE Trans. on Pattern Analysis and Machine Intelligence, 20(8):832844.
  46. S. Segrera, M. Moreno. Multiclassifiers: applications, methods and architectures. Proc. of InternationalWorkshop on Practical Applications of Agents and Multiagents Systems, IWPAAMS05, 263–271, 2005.
  47. H. Ahn, H. Moon, M.J. Fazzari, N. Lim, J.J. Chen and R.L. Kodell. Classification by ensembles from random partitions of high dimensional data. Computational Statistics and Data Analysis, 51:6166–6179, 2007.
  48. M. Hamza and D. Larocque. An empirical comparison of ensemble methods based on classification trees. Statistical Computation & Simulation 75(8):629-643, 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Osteoporosis Disease Multi-Classifier Decision Trees Prediction features reduction

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