Presentation for my paper in #rcis2016. An information systems conference in Grenoble in France. In it I use neural networks to discover novel abstract features from dementia data.

1. Variable Interactions in Risk
Factors for Dementia
Jim O’ Donoghue, Mark Roantree and Andrew McCarren
Research funded by:
European Union Seventh Framework Programme, grant agreement number 304979
and Science Foundation Ireland, grant agreement number SFI/12/RC/2289.

2. 25% Introduction
50% Approach
90% Experiments
100% Conclusions 2

3. 3

4. Dementia
4

5. Dementia
1 Alzheimer Europe, The prevalence of Dementia in Europe, Online: http://www.alzheimer-
europe.org/Policy-in-Practice2/Country-comparisons/The-prevalence-of-dementia-in-
Europe; Last Accessed 30-05-16 .
5

6. Dementia
2 Alzheimer Society, Dementia 2014 Report: Opportunity for Change; Online:
https://www.alzheimers.org.uk/dementia2014; Last Accessed 30-05-16.
6

7. Dementia
2 Alzheimer Society, Dementia 2014 Report: Opportunity for Change; Online:
https://www.alzheimers.org.uk/dementia2014; Last Accessed 30-05-16.
7

8. In-Mindd
8

9. In-Mindd
9
FP7
Project

10. In-Mindd
10
In novative
Mi d-life
In tervention for
D ementia
D eterrence
FP7
Project

11. In-Mindd
11

12. In-Mindd
12

13. In-Mindd
13

14. Lower dementia risk
in middle-age (40-60)
In-Mindd
14

15. Lower dementia risk
in middle age (40-60)
Modifiable Dementia
Risk+Protective Factors
In-Mindd
15

16. Evaluate dementia factors
16

17. Evaluate dementia factors
Improve dementia survival
predictions
17

18. Evaluate dementia factors
Improve survival predictions
Determine factor
interactions
18

19. 19
:7 factor combinations
tested

20. 20
:7 factor combinations tested
:Neural network surivival
analysis

21. 21
:7 factor combinations tested
:Neural network surivival analysis
:Candidate interactions
found

22. 22
Test dementia factors
:7 combinations tested
Improve survival predictions
:Neural networks
Determine factor interactions
:Hidden layer analysis

23. 23

24. Process
1. Hyper-Parameter Configuration
24

25. Process
1. Hyper-Parameter Configuration
2. Parameter optimisation
25

26. Process
2. Parameter optimisation
1.
2.
3.
4.
5.
26

27. Process
2. Parameter optimisation
1. initialise architecture
2.
3.
4.
5.
27

28. Process
2. Parameter optimisation
1. initialise architecture
2. construct hypothesis
3.
4.
5.
28

29. Process
2. Parameter optimisation
1. initialise architecture
2. construct hypothesis
3. build cost
4.
5.
29

30. Process
2. Parameter optimisation
1. initialise architecture
2. construct hypothesis
3. build cost
4. construct model
5.
30

31. Process
2. Parameter optimisation
1. initialise architecture
2. construct hypothesis
3. build cost
4. construct model
5. train
31

32. 32
Input
Visible Layer
h(1)Hidden
Layer
𝑥
Output
Visible Layer 𝑜

33. 33
Input
Visible Layer
h(1)
……𝑥2
Hidden
𝑥1 𝑥 𝑛𝑥0
Layer
𝑥
Output
Visible Layer 𝑜
W(1)

34. 34
Input
Visible Layer
h(1)
……𝑥2
Hidden
𝑥1 𝑥 𝑛𝑥0
Layer
𝑥
Output
Visible Layer 𝑜
W(1)

35. 35
Input
Visible Layer
h(1)
……𝑥2
Hidden
𝑥1 𝑥 𝑛𝑥0
Layer
𝑥
Output
Visible Layer 𝑜
W(1)

36. 36
Input
Visible Layer
h(1)
……𝑥2
Hidden
𝑥1 𝑥 𝑛𝑥0
Layer
𝑥
Output
Visible Layer 𝑜
W(1)

37. 37
a(1)
0
Input
Visible Layer
h(1)
……𝑥2
a(1)
o…a(1)
2a(1)
1
Hidden
𝑥1 𝑥 𝑛𝑥0
Layer
𝑥
Output
Visible Layer 𝑜
W(1)

38. 38
a(1)
0
Input
Visible Layer
W(1)
h(1)
……𝑥2
a(1)
o…a(1)
2a(1)
1
W(2)
Hidden
𝑥1 𝑥 𝑛𝑥0
Layer
𝑥
D1
Output
Visible Layer
CD2S
𝑜

39. Classifications
S -> Surivival
D1 ->Dementia
D2 -> Death (without dementia)
C -> Censorship (study drop-out)
39

40. a(1)
0
Input
Visible Layer
W(1)
h(1)
……𝑥2
a(1)
o…a(1)
2a(1)
1
W(2)
Hidden
𝑥1 𝑥 𝑛𝑥0
Layer
𝑥
D1
Output
Visible Layer
CD2S
𝑜
40

41. a(1)
0
Input
Visible Layer
W(1)
h(1)
……𝑥2
a(1)
o…a(1)
2a(1)
1
W(2)
Hidden
𝑥1 𝑥 𝑛𝑥0
Layer
𝑥
D1
Output
Visible Layer
CD2S
𝑜
41

42. 42

43. Variables
Maastricht Ageing Study (MAAS)
840x25 subset
-> 15 binary
-> 9 continuous/discrete
-> 1 derived
43

44. Combinations
14 factors
-> 3 non-modifiable
-> 11 modifiable
44

45. Combinations
14 factors
-> 3 non-modifiable
- Age
- Gender
- Education before 21
45

46. Combinations
14 factors
-> 11 modifiable
3 protective
- Cognitive ativity
- Physical activity
- Moderate alcohol use
46

47. Combinations
14 factors
-> 11 modifiable
8 risk
- Smoking
- Mid-life obesity
- Mid-life hypertension
- Diabetes
47
- Cholesterol
- Cardiovascular
disease
- Kidney disease
- Depression

48. Combinations
14 risk factors
-> 11 modifiable
-> 3 non-modifiable
7 combinations tested
-> 3 baseline no relative risk weight
-> 4 with relative risk
48

49. Combinations
without relative risk weights
B1 = BinaryBaseline1
B2 = BinaryBaseline2
CB = ContinuousBaseline
49

50. Combinations
without relative risk weights
B1 + B2:
11 binary modifiable;
adjusting for age, sex + education
50

51. Combinations
without relative risk weights
B1 + B2:
11 binary modifiable;
adjusting for age, sex + education
dementia yes/no vs. multi-class
51

52. Combinations
without relative risk weights
B1 + B2:
binary modifiable; adjusting;
dementia yes/no vs. multi-class
CB:
6 binary; ;
52

53. Combinations
without relative risk weights
B1 + B2:
binary modifiable; adjusting;
dementia yes/no vs. multi-class
CB:
6 binary; ;
modifiable; adjusting; multi-class
53

54. Combinations
with relative risk weights
BW1
BW2
BW3
CW
54

55. Combinations
with relative risk weights
BW1: 11 binary modifiable; adjusting
BW2
BW3
CW
55

56. Combinations
BW1: 11 binary modifiable; adjusting
BW2: 11 bin. mod. adj.
BW3
CW
56

57. Combinations
BW1: 11 binary mod. adj.
BW2: 11 bin. mod. adj.
BW3: 11 bin. mod.
CW
57

58. Combinations
BW1: 11 binary mod. adjusted
BW2: 11 mod. adj.
BW3: 11 mod.
CW: 6 binary; 5 continuous
58

59. batch size (1, 50)
n hidden nodes (2, 20)
learning rate (0.0001, 0.3)
regularisation (0.0001, 0.1)
max epochs (100, 2000)
max iterations (5000, 100000)
59

60. batch size (1, 50)
n hidden nodes (2, 20)
learning rate (0.0001, 0.3)
regularisation (0.0001, 0.1)
max epochs (100, 2000)
max iterations (5000, 100000)
60

61. 61

62. Accuracy
0.56
0.59
0.62
0.65
0.68
0.71
B2-sd CB-sd BW1 BW2-sd BW3 CW-sd62

63. 0.0187
0.0002
0.0221
0.003
0.0001 0.0002
B2-sd CB-sd BW1 BW2-sd BW3 CW-sd
Predictive Significance
63

64. 64
Risk Interactions
-0.39178
-0.004
-0.003
-0.002
-0.001
0
0.001
0.002
0.003
0.004
h1 h2 h3 h4 h5 h6
age_risk educ_risk cog_act
phys_inact obese diabetes
di_alcohol smokes depressed
hyperT cholesterol cvd
kidney

65. 65
Risk Interactions
-0.39178
-0.004
-0.003
-0.002
-0.001
0
0.001
0.002
0.003
0.004
h1 h2 h3 h4 h5 h6
age_risk educ_risk cog_act
phys_inact obese diabetes
di_alcohol smokes depressed
hyperT cholesterol cvd
kidney

66. 66
Node Weights
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
h1 h2 h3 h4 h5 h6

67. 67

68. Neural network framework
68

69. Neural network framework
HP optimisation
Hidden layer +
Non linear survival analysis
69

70. Neural network framework
Confirm and improve predictions
Candidate risk interactions
70

71. Neural network framework
Confirm and improve predictions
Candidate risk interactions
Continuous Data
+ Relative Risk Weight Importance
71

72. jim.odonoghue@insight-centre.org
72

73. jim.odonoghue@insight-centre.org
Research funded by:
European Union Seventh Framework Programme, grant agreement number 304979
and Science Foundation Ireland, grant agreement number SFI/12/RC/2289.

74. Middle-aged individuals
(40 – 60)
In-Mindd
74

75. Middle-aged individuals
(40 – 60)
Risk Profiler +
Support Environment
In-Mindd
75

76. Middle-aged individuals (40 – 60)
Risk Profiler +
Support Environment
Dementia Risk Factors
In-Mindd
76

77. Sensitivity/Recall
0.35
0.45
0.55
0.65
0.75
0.85
0.95
B2-sd CB-sd BW1 BW2-sd BW3 CW-sd77

78. Specificity
0.35
0.45
0.55
0.65
0.75
0.85
0.95
B2-sd CB-sd BW1 BW2-sd BW3 CW-sd78

79. Precision
0.45
0.48
0.51
0.54
0.57
0.6
0.63
0.66
B2-sd CB-sd BW1 BW2-sd BW3 CW-sd79

80. Area Under the Curve
0.68
0.7
0.72
0.74
0.76
0.78
B2-sd CB-sd BW1 BW2-sd BW3 CW-sd80