Building a disease risk model of osteoporosis based on traditional Chinese medicine symptoms and western medicine risk factors

被引:18
作者
Zhou, X. H. [2 ,3 ,4 ]
Li, S. L. [2 ]
Tian, F. [1 ]
Cai, B. J. [2 ]
Xie, Y. M. [1 ]
Pei, Y. [2 ]
Kang, S. [5 ]
Fan, M. [2 ]
Li, J. P. [1 ]
机构
[1] China Acad Chinese Med Sci, Inst Basic Res Clin Med, Beijing 100700, Peoples R China
[2] Renmin Univ China, Sch Stat, Beijing 100872, Peoples R China
[3] VA Puget Sound Hlth Care Syst, HSR&D Ctr Excellence, Seattle, WA 98101 USA
[4] Univ Washington, Dept Biostat, Seattle, WA 98195 USA
[5] Beijing Univ Chinese Med, Dongzhimen Hosp, Beijing 100700, Peoples R China
基金
美国国家科学基金会;
关键词
GPLM; SVM-RFE; random forest; association rule learning; TCM symptoms; PARTIAL LINEAR-MODELS; FRACTURE;
D O I
10.1002/sim.4382
中图分类号
Q [生物科学];
学科分类号
090105 [作物生产系统与生态工程];
摘要
In the Traditional Chinese Medicine (TCM) cross-sectional survey conducted by our team, we were interested in determining the risk factors of osteoporosis. To analyze this TCM study, we had to deal with three statistical problems: (1) a very large number of potential risk factors, (2) interactions among potential risk factors, and (3) nonlinear effects of some continuous-scale risk factors. To address these analytic issues, we used two data mining methods, support vector machine recursive feature elimination and random forest; to deal with the curse of high-dimensional risk factors, we applied another data mining technique of association rule learning to discover the potential associations among risk factors. Finally, we employed the generalized partial linear model (GPLM) to determine nonlinear effects of an important continuous-scale risk factor. The final GPLM model shows that TCM symptoms play an important role in assessing the risk of osteoporosis. The GPLM also reveals a nonlinear effect of the important risk factor, menopause years, which might be missed by the generalized linear model. Copyright (c) 2012 John Wiley & Sons, Ltd.
引用
收藏
页码:643 / 652
页数:10
相关论文
共 23 条
[1]
Agrawal R., 1993, SIGMOD Record, V22, P207, DOI 10.1145/170036.170072
[2]
SmcHD1, containing a structural-maintenance-of-chromosomes hinge domain, has a critical role in X inactivation [J].
Blewitt, Marnie E. ;
Gendrel, Anne-Valerie ;
Pang, Zhenyi ;
Sparrow, Duncan B. ;
Whitelaw, Nadia ;
Craig, Jeffrey M. ;
Apedaile, Anwyn ;
Hilton, Douglas J. ;
Dunwoodie, Sally L. ;
Brockdorff, Neil ;
Kay, Graham F. ;
Whitelaw, Emma .
NATURE GENETICS, 2008, 40 (05) :663-669
[3]
Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[4]
Breiman L, 1998, OUT OF BAG EST UNPUB
[5]
Cheng SD, 2006, HUANGDIS INTERNAL CL, P410
[6]
Diaz-Uriarte R, 2005, 5 ANN SPAN BIOINF C
[7]
Fang ZH, 2007, CHINESE J INFORM TRA, V14, P15
[8]
Gao LP, 2007, J FUJIAN COLL TCM, V17, P13
[9]
Gene selection for cancer classification using support vector machines [J].
Guyon, I ;
Weston, J ;
Barnhill, S ;
Vapnik, V .
MACHINE LEARNING, 2002, 46 (1-3) :389-422
[10]
Guyon I., 2006, Stud Fuzziness Soft Comput