102 6

101

375

Fried

(data mining techniques)

:

IADL()

SF36--

IADLSF36--

:

67.5%

0.605

:

!

!

2013.6

..1

...1

...3

...3

..4

..4

...8

.11

15

...15

...17

...18

...19

23

26

...27

...35

...37

...39

...44

...52

56

59

59

60

61

...............................................................................63

..64

...............................................69

/...90

/...92

3-1 16

3-2 18

3-3.26

4-5-5 -.51

4-5-6 -.52

2-1 ...6

2-2..9

3-4-1.22

3-4-2.23

4-1-1. ...28

4-1-2 ....32

4-1-3 () ....34

4-2 .......36

4-3 ...38

4-4...41

4-5-1.48

4-5-2 Confidece Factor..50

4-5-3 M1-1050

4-5-4 C=0.4M=6.51

4-6-1 53

4-6-2 54

4-6-3 55

4-7-156

4-7-2 ...58

5-1 .61

5-2 ...62

1

8265

9165

9.01% 44.17% 101 65

11.15% 76.2 10 32.0

65R.E.

Pel-littel(2009)

Palmer RM(1999)Mark A. Sager(1998)

(frail)

65 11%

34%10 121%

2

(2005)

Fried

(2001)(unintentional weight loss)

(low grip strength) (poor energy)

(slowness)(low physical activity)

(2010)

3

65( 65)

()

4

"

Woodhouse KW(1988)

MacAdamWilliams

(1989)Rockwood(1994)

5

Sarlasian(1996)

:

Woodhouse(1998) 65

Bortz

(2002)

Markle (2003)

HoganFriedChinMitnitskiLipsitz

(1994-2003)

()

HoganFriedChinMitnitski

Rockwood

(2002)Song

Mitnitski RockWood(20022004)

(2006)(

6

)

2-1

2-1

()

Woodhouse KW

(1988)

MacAdamWilliams

(1989)

Sarlasian

(1996)

John(1997)

Woodhouse (1998) 65

Bortz(2002)

7

Markle(2003)

Hogan(2003)

(1)

(2)

8

Fried Frailty Index(2001)

Fried LP(2001)frailty phenotype

Jones(2004)

(1)(2)(3)

(4)(5)(6)(7)(8)(

)(9)Rockwood(2005) 70

Ranieri P(1991-

2006):(1)(2)

(3)(4)(5)(6)

(2006)

9

Rolfson(2006)(1)(2)

(3)(4)(5)(6)

(7)(8)Ensrud(2008)

Vermeulen1

(2011)

(1)

Fried(2001)(2)

2-2

2-2

()

Fried LP(2001) (frailty

phenotype

Jones(2004) (1)(2)(3)

10

(4)(5)(6)

(7)(8)()

(9)

Rockwood(2005)

70

Rolfson(2006) (1)(2)

(3)(4)

(5)(6)

(7) (8)

RanieriP

(2006)

Ensrud(2008)

(2010)

(1)(2)(3)(4)

(5)(6)(7)

(8)(9)

(10)

11

Vermeulenl

(2011)

Fried

:

Usama Fayyad

Fayyad( Pattern Recognition)

1991(Data Mining) 1996

Fayyad

1995Gruppe&Owrange

1997

Berry Linoff :

12

(interdisciplinary field)

(Artificial Intelligence)

(computer science)(machine learning)

(data visualization)(Stastics)

Berry Linoff (2008)

1. (Classification)

:

2. (Estimation)

:

13

3. (Prediction)

:

4. (Association Rule)

:

Apriori

5.(Clustering)

: K-means

6.Description :

(supervised) (unsupervised)

14

:

Freeman Skapura (1992)

(Neuron)

(Leave node)

(,2009) ID3C4.5C5.0CART

CHAID C4.5

15

(2001)

(2007)(2008)

MDA

(2011)

3-1

()(ADL&IADL)

http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/ccd=Nt1CHr/search?q=auc=%22%E6%9D%8E%E9%9B%85%E9%9B%AF%22.&searchmode=basichttp://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/ccd=Nt1CHr/search?q=auc=%22%E6%9D%8E%E9%9B%85%E9%9B%AF%22.&searchmode=basic

16

(SF-36)-

-

3-1

Fried

()

(&)

(ADL&IADL)

(&)

(ADL&IADL)

(GDS-15)

(AD-8)

SF36-

SF36--

SF36--

17

3-2

1:

2:

3:

4:

5:

6:

7:

8:

18

3-2

: 65

()(

)

2012 10 18

2013 4 15 375

19

101

()

3-4-1 Fried

13

136

1.

2.

(LSNS-A)

3.

20

4.

5.

6. (IADL)

7. (ADL)

8.

(MNA)

9. (AD-8)

10. (SF-36)

11. (GDS-15)

12. (OHIP)

13.

16

MNA(Cronbachs -0.538) SF-36

21

-(Cronbachs -0.586)

Cronbachs 0.7

(2000) Cronbachs 0.5 0.7

(Cardiovascular Health Study) Fried 2001

()

(frailty) 3-4-2

(1) (weight loss)(MNA)

1

(2) (grip strength)(TTM-YD, Japan)

20%

(3) (walk time)

20%

22

(4) (exhaustion)(SF-36)

3-4-1

(GDS-15)

0~20

(AD-8) 0.26

(MNA) 0~31

(ADL

)

0~100

() 0~10

(SF-36) 0~100

Charlson score 0

1

2

-

(SF-36)

0~100

(IADL)

()

-

(SF-36)

0~100

23

3-4-2

()()

1

159.97(cm) 8.94(s)

24

3-5-1

1:

(ADLSF36-

) Discretize

2:

3:

J48

Confidence Factor 0.1~1.0

()

Confidence Factor 0.4

25

(M)1-10

()

(M) 6 10 10-fold cross-

validation 10 91

()(

)

4:

5: 16 375

16

26

6:

7:

8:

3-3

27

375 4-1-1 74.75

SD 6.893 65-69 26.6%70-74

24.5%75-79 80 24.5%

50.9% 49.1% 88.5% 7.2%

3.7% 35.2% 32.8%

1.6%()

64.3%() 35.5% 14.7%()

21.1% 62.9%

24.8% 12%

87.5%

52.5%

21.3% 13.6%

8.3% 46.7%

41.1% 11.8%

71.5% 28.5%

28

4-1-1.

374

65-69

70-74

75-79

80

74.75

6.893

100

90

92

92

26.6%

24%

24.5%

24.5%

375

191

184

50.9%

49.1%

375

332

27

14

2

88.5%

7.2%

3.7%

0.5%

375

91

24.3%

29

132

15

4

4

123

6

35.2%

4%

1.1%

1.1%

32.8%

1.6%

()

()

()()

()()

375

74

34

133

55

39

40

19.7%

9.1%

35.5%

14.7%

10.4%

10.7%

373

236

93

44

62.9%

24.8%

11.7%

373

45

328

12%

87.5%

30

359

197

80

51

31

52.5%

21.3%

13.6%

8.3%

373

175

154

44

46.7%

41.1%

11.8%

375

268

107

71.5%

28.5%

4-1-2 18.7%

81.3% 12.5%

87.5% 35.5% 63.5%

49.6% 50.4%

6.4% 83.7%

31

10.4% 89.6%

71.9% 28.1%

4-1-3() 1.88SD 2.296

(ADL ) 96.81 SD 10.964

3.2% 96.8%IDSL

85.3% 14.7%

15.5% 82.1%>=3.0

2.1% 12.3% 1.0-3.0 11.0%

1.3 %(GDS-15 ) 95%

3.6% 1.4% 7.44 SD

4.636 30.64kg 17.92kg

SF36 79.15 SD 23.66

- 86.40 SD 32.00

- 86.66 SD 17.23

86.66 SD 17.241

82.65 SD 21.15 71.50 SD

20.28 75.14 SD 15.73

63.01SD 18.75

32

4-1-2

Charlson score 373

0

1

2

230

117

26

61.7%

31.4%

7.0%

375

70

305

18.7%

81.3%

375

47

328

12.5%

87.5%

371

133

238

35.5%

63.5%

133

66

67

49.6%

50.4%

338

24

6.4%

33

314 83.7%

375

39

336

10.4%

89.6%

32

23

9

71.9%

28.1%

375

12

363

3.2%

96.8%

>=3.0

1.0-3.0

374

8

46

41

274

5

2.1 %

12.3%

11.0%

73.3%

1.3 %

(IADL)

373

318

55

85.3%

14.7%

(AD-8) 366

34

58

308

15.5%

82.1%

(GDS-15)

362

344

13

5

95%

3.6%

1.4%

4-1-3 ()

() 348 1.88 2.303

352 7.44 4.636

187

177

30.64

17.92

8.313

5.855

(ADL) 365 96.81 10.964

(SF-36)

-

-

375

375

373

373

79.15

86.40

86.66

86.66

23.66

32.00

17.23

17.241

35

372

371

370

369

375

82.65

71.50

75.14

63.01

23.61

20.28

17.02

15.73

18.75

3.250

4-2

IADL

(SF-36) Cronbachs

0.90 0.9150.9170.931(ADL)

AD-8 GDS-

15 Cronbachs 0.80 0.8940.856

0.8860.8670.880

Cronbachs 0.70 0.777

0.7700.758(MNA) Cronbachs

0.5380.586(2000)

Cronbachs 0.5 0.7

36

(Cronbachs )

IADL 0.915 7

ADL 0.894 10

MNA 0.538 18

AD-8 0.856 8

SF-36 0.917 10

-

0.931 4

-

0.886 3

0.586 2

0.867 2

0.777 4

0.770 5

0.758 5

GDS-15 0.880 15

Cronbachs >0.7

4-2

37

4-3 74.08 SD

6.75 74.08 SD 6.75 73.5%

32.2% 26.5% 67.8%

85.9% 90.7% 10.0%

4.9% 2.9% 4.4% 1.2%

10.0%

27.8% 8.8%

9.3%() 38.8% 32.7%()

16.5% 13.2%()()

11.2% 9.8%()() 14.7%

7.3%

(P

38

4-3

Mean(SD)

Mean(SD)

74.08(6.75) 75.31(6.98) 0.095

()

()

P

125(73.5)

45(26.5)

66(32.2)

139(67.8)

0.001

()

()

146(85.9)

17(10.0)

5(2.9)

2(1.2)

17(10.0)

15(8.8)

66(38.8)

28(16.5)

186(90.7)

10(4.9)

9(4.4)

0(0.0)

57(27.8)

19(9.3)

67(32.7)

27(13.2)

0.432

0.654

39

()()

()

()

19(11.2)

25(14.7)

20(9.8)

15(7.3)

Fried

(P

40

2.16SD 2.43(SF)

(SF)(SF)

79.88SD 36.89

1.17SD 2.24

(IADL)

(P

41

4-4

Mean(SD)

Mean(SD)

159.97cm

()

159.97cm

-

-

Charlson score

ADL

5.35(2.10)

6.63(3.20)

33.88(7.18)

20.67(7.83)

1.50(2.21)

89.14(16.04)

93.68(21.08)

89.14(16.04)

23.91(3.16)

0.414(0.65)

98.02(7.38)

10.54(60)

10.42(6.30)

24.55(6.76)

17.17(4.96)

2.16(2.43)

84.63(17.94)

79.88(36.89)

84.63(17.94)

23.37(3.31)

0.554(0.83)

95.78(13.19)

0.001

0.029

0.011

0.006

0.001

0.006

0.096

0.118

0.118

42

0.54(1.69)

0.28(0.91)

1.17(2.24)

1.06(2.43)

0.001

0.001

()

()

P

5(3.3)

148(96.7)

92(55.1)

75(44.9)

13(7.6)

157(92.4)

29(17.1)

141(82.9)

18(10.6)

152(89.4)

44(25.1)

131(74.9)

195(95.1)

10(4.9)

26(12.7)

179(87.3)

41(20.0)

164(80.0)

29(14.1)

176(85.9)

0.001

0.001

0.112

0.467

0.300

43

IADL

5(3.3)

148(96.7)

49(29.2)

119(70.8)

0(0.0)

170(100.0)

15(8.9)

154(91.1)

19(10.2)

167(89.8)

84(41.2)

120(58.8)

12(5.9)

193(94.1)

40(19.6)

164(80.4)

0.013

0.016

0.001

0.004

44

4-5-1

5 5 320 5

28 (ADL ) 55 55

755 358(SF-36)

56.256.2 3356.2 340

(SF-36) 5050 5850

317 (SF-36 ) 56.2 56.2

3356.2 340

3.3 6.73.3 275

3.4-6.7 576.7 16

(ADL) 40 7040 541-70

45

670 354(SF-36)

41.7 70.841.7 541.8-70.8

5470.8 314(SF-36)

33.3 66.733.3 4533.4-66.7

1366.7 317(SF-36)

41.6 70.841.6 541.7-70.8 54

70.8 100

2.557.52.5 244

2.6-5 775.1-7.5 137.5 14

(ADL) 32.55577.532.5

432.6-55 356-77.5 577.5

353(SF-36) 34.456.3

78.134.4 234.5-56.3 3156.4-

78.1 8178.1 259(SF-36)

25507525 4526-50 13

51-75 1375 304(SF-36

) 34.456.378.134.4 234.5-

46

56.3 3156.4-78.1 8178.1 259

24682 2443-4

545-6 347-8 128

4(ADL) 28466482

28 429-46 247-64 2

65-82 482 352(SF-36

) 3047.56582.530 2

31-47.5 347.6-65 5465.1-82.5

5582.5 259(SF-36)

20406080 20 34 21-40 11

41-60 1361-80 1380 304

(SF-36) 3047.56582.530

231-47.5 347.6-65 54

65.1-82.5 5582.5 25 4-6-1

C4.5 Confidence

Factor 0.1~1.0 4-5-2

47

Confidence Factor 0.4 0.512

(M)1-10 M 6

0.566 1010-fold cross-

validation 4-5-3

- 50

IADL

17

17

4-5-4

4-5-5

48

4-5-1

-2 -3

()

0-5 320

()

0-3.3 275

6-10 28 3.4-6.7 57

6.8-1.0 16

(ADL)

0-55 7

(ADL)

0-40 5

41-70 6

56-100 358

71-100 354

(SF-36)

0-56.2 33

(SF-36)

0-41.7 5

56.3-100 340 41.8-70.8 54

70.9-100 314

(SF-36)

0-50 58

(SF-36)

0-33.3 45

51-100 317 33.4-66.7 13

66.8-100 317

(SF-36)

0-56.2 33

(SF-36)

0-41.6 5

56.3-100 340 41.7-70.8 54

70.9-100 314

49

-4 -5

()

0-2.5 244

()

0-2 244

2.6-5 77 3-4 54

5.1-7.5 13 5-6 34

7-8 12

7.6-10 14 8-10 4

(ADL)

0-32.5 4

(ADL)

0-28 4

32.6-55 3 29-46 2

56-77.5 5 47-64 2

65-82 4

77.6 353 83-100 352

(SF-36)

0-34.4 2

(SF-36)

0-30 2

34.5-56.3 31 31-47.5 3

47.6-65 54

56.4-78.1 81 65.1-82.5 55

78.2-100 259 82.6-100 259

(SF-36)

0-25 45

(SF-36)

0-20 34

26-50 13 21-40 11

51-75 13 41-60 13

75-100 304 61-80 13

81-100 304

(SF-36)

0-34.4 2

(SF-36)

0-30 2

34.5-56.3 31 31-47.5 3

56.4-78.1 81 47.6-65 54

78.2-100 259 65.1-82.5 55

82.6-100 259

50

4-5-2 Confidece Factor

C 0.1 0.2 0.3 0.4 0.5

0.488 0.473 0.483 0.512 0.493

C 0.6 0.7 0.8 0.9 1.0

0.473 0.473 0.473 0.473 0.473

C 0.1 0.2 0.3 0.4 0.5

0.429 0.459 0.488 0.502 0.488

C 0.6 0.7 0.8 0.9 1.0

0.483 0.483 0.483 0.483 0.483

C 0.1 0.2 0.3 0.4 0.5

0.444 0.473 0.473 0.502 0.493

C 0.6 0.7 0.8 0.9 1.0

0.463 0.463 0.463 0.463 0.463

C 0.1 0.2 0.3 0.4 0.5

0.478 0.454 0.459 0.483 0.498

C 0.6 0.7 0.8 0.9 1.0

0.493 0.493 0.493 0.493 0.493

4-5-3 M1-10

M 1 2 3 4 5

0.488 0.561 0.537 0.537 0.566

M 6 7 8 9 10

0.566 0.556 0.556 0.537 0.537

51

4-5-4 C=0.4M=6

4-5-5 -

0.6 0.58 0.54 0.57 0.53 0.59 0.61 0.54 0.56 0.58 0.57

0.58 0.62 0.58 0.62 0.69 0.65 0.61 0.7 0.69 0.59 0.63

(%)

58.9 59.5 56.0 58.9 60.3 61.9 60.8 61.1 61.3 57.9 59.7

52

4-5-6 -

4-6-1

0.674 0.676

67.5

53

4-6-1

(%)

0.674 0.676 67.5

0.646 0.619 63.4

0.646 0.619 63.4

4-6-2 4-6-3

()

(P

54

4-6-2

B

S.E

OR

95%C.I

Lower

limit

Upper

limit

0.166 0.506 1.180 0.438 3.182

-0.369 0.402 0.691 0.314 1.521

-0.232 0.433 0.793 0.339 1.853

1.001 0.630 2.722 0.792 9.349

0.245 0.278 1.278 0.741 2.204

0.045 0.070 1.046 0.912 1.199

-20.495 10294.674 0.000 0.000 .

SF-36

-0.004 0.009 0.996 0.978 1.015

SF-36

-

-0.010 0.006 0.990 0.980 1.001

55

* P

56

SF-36

-

-.007 .005 .993 .983 1.002

.150 .107 1.162 .943 1.433

*

.219 .109 1.245 1.005 1.543

.351 .436 1.420 .604 3.338

* P

57

(P

58

4-7-2

3 -

1

2 1

SF36-

-

2

0

2 0

2

CCI

0

1

ADL

0

IADL 1 0

SF36-

1

0

59

WEKA

16

375 16

( 4-7-1)

60

(4-3) J Woo

Rockwood

IADL()

Fried

5-1

61

5-1

Mean(SD) 0.28(0.91) 2.87(3.15)

Mean(SD) 1.06(2.43) 7.78(4.48)

Mean(SD) 23.91(3.16) 26.37(1.88)

Mean(SD) 23.37(3.31) 23.21(2.51)

IADL

Mean(SD) 13.69(1.44) 5.82(1.87)

Mean(SD) 12.88(2.48) 4.31(2.00)

( 4-7-2)

SF36--

IADLSF36-

-

5-2

62

5-2

St John PD, Tyas SL

Kenneth Rockwood

(2005),

Terri Blackwell(2006)

(2010)

SF36-

SF36--

SF36--

J. Woo(2005)

IADL Fried(2001)IADL

(2010)

63

(P

64

67.5% 0.605

1. Berry &Linoff ,Data Mining Techniques, 2008

2. Bortz WM: A conceptual framework of frailty: a review.2002

3. Chin A Paw MJ, Dekker JM, Feskens EJ, Schouten EG, Kromhout

D.How to select a frail elderly population? A comparison of three

working definitions. J Clin Epidemiol 1999;52:1015-21.

4. F. Sebastiani, "Machine learning in automated text categorization,"

ACM computing

5. Freeman, J. A. and D. M. Skapura, Neural Networks

Algorithms,Applications, and Programming Techniques, Addison-

Wesley,Reading, Michigan ,1992.

65

6. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener

J,Seeman T, Tracy R, Kop WJ, Burke G, McBurnie MA.

Cardiovascular Health Study Collaborative Research Group. Frailty

in older adults:evidence for a phenotype. J Gerontol A Biol Sci Med

Sci.56(3):M146-M156, 2001.

7. Hogan DB, MacKnight C, Bergman H; Steering Committee,

Canadian Initiative on Frailty and Aging. Models, definitions, and

criteria of frailty[review]. Aging Clin Exp Res

8. Joan Vermeulen,Jacques CL Neyens,Erik van Rossum,Marieke D

Spreeuwenberg and Luc P de WittePredicting ADL disability in

community-dwelling elderly people using physical frailty indicators:

a systematic review2011

9. Jones DM,Song X,?Rockwood K:Operationalizing a frailty index

from a standardized comprehensive geriatric assessment..2004

10. J. Woo,W. Goggins, A. Sham,S.C. Ho :Social Determinants of

Frailty2005

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Med.?1998

1.

2008

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2007

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3.

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reserved.(IQOLA SF-36 Taiwan Standard Version 1.0)

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90

91

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92

93

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20-30

07-3121101#2781#18

94

07-3121101

6646 07-3133525

1