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Detecting Dementia from Written and Spoken LanguageMSc Presentation by: Vaden Masrani Supervisor: Giuseppe Carenini
December 20th, 2017
2
• Recent interest in intersection of computational linguistics and clinical psychology
• Can we detect: • Depression from Facebook updates • PTSD from tweets
• Is it possible to detect dementia from interview transcripts?
• Why do we care?
Background
ContributionsBackground Medical
Study 2Study 1 ConclusionStudy 3
• What is dementia? • Broad category of brain diseases which cause decrease in mental ability
• Alzheimer’s disease • vascular dementia • dementia with Lewy Bodies, and others
• Alzheimer’s is one of the most costly diseases in developing countries
• 47 million people worldwide w/ dementia • One in nine people aged 65 and older have AD • $109 - $215 billion annually in US
3
Background
ContributionsBackground Medical
Study 2Study 1 ConclusionStudy 3
“Researchers believe that early detection of Alzheimer’s will be key to preventing, slowing and stopping the disease.”
4
A. Association. 2016 alzheimer’s disease facts and figures. https://www.alz.org/documents custom/2016-facts-and-figures.pdf, 2016
Background
ContributionsBackground Medical
Study 2Study 1 ConclusionStudy 3
• Currently diagnosis is: • Expensive • Time consuming • Invasive • Often goes undiagnosed, especially in developing countries
• Symptoms: • Wide variety of symptoms severe enough to affect daily
functioning • Aphasia: Speech and language difficulty
5
Background
ContributionsBackground Medical
Study 2Study 1 ConclusionStudy 3
6
• Our work builds upon Fraser 2015 • Address small data issue • Introduce a new feature set • Extend to written samples
Background• State of the art in
detecting AD was achieved by Fraser 2015* • Introduced a large (370) feature set • Achieved state-of-the-art results on
DementiaBank dataset
“Cookie Theft” Photo from the Boston Diagnostic Aphasia
Examination
ConclusionContributionsBackground Previous Work
Study 2Study 1 Study 3
*Fraser, Kathleen C., Jed A. Meltzer, and Frank Rudzicz. "Linguistic Features Identify Alzheimer’s Disease in Narrative Speech." Journal of Alzheimer's Disease 49.2 (2015): 407-422. APA
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A clock drawn by a person with hemispatial neglect
Study 1: New feature sets• Fraser missed two feature sets
• One from the medical literature • One from the NLP literature
• We replicate their findings and show: • Measuring hemispatial neglect achieves new
state-of-the-art • Discourse analysis has no effect on
DementiaBank dataset
Thalia Shoshana Field, Vaden Masrani, Gabriel Murray, Giuseppe Carenini. "Improving Diagnostic Accuracy Of Alzheimer's Disease From Speech Analysis Using Markers Of Hemispatial Neglect." Alzheimer's & Dementia: The Journal of the Alzheimer's Association 13.7 (2017): P157-P158.
Contributions New feature sets
ConclusionBackground Study 2Study 1 Study 3
8
Study 2: Written Language• All previous work has been on spoken samples
• Written samples harder to process but… • There will be lots of data in the future
• We create a publicly available dataset of blog posts • Posts written by people with / without dementia • Allows for:
• Analysis of changes in writing style as disease progresses • Comparison between different pathologies
• We show it is possible to detect dementia from writing samples
Vaden Masrani, Gabriel Murray, Thalia Shoshana Field, Giuseppe Carenini. "Detecting Dementia through Retrospective Analysis of Routine Blog Posts by Bloggers with Dementia." BioNLP (2017).
ConclusionBackground Study 2Study 1 Study 3Contributions Written language
9
Study 3: Detecting Mild Cognitive Impairment
Vaden Masrani, Gabriel Murray, Thalia Shoshana Field, Giuseppe Carenini "Domain Adaptation for Detecting Mild Cognitive Impairment." Canadian Conference on Artificial Intelligence (2017).
• MCI is difficult to detect • Very little data (n=37) • Less symptomatic than AD • Less studied than AD
• We show how to overcome the lack of data • Idea: Use AD data to detect MCI • Technique: domain adaptation • We compare two algorithms across a range of models
ConclusionBackground Study 2Study 1 Study 3Contributions
MCI w/ Domain Adaptation
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ConclusionContributionsBackground Study 2 Study 3
Study 1 Hemispatial
Neglect
Study 1
11
Hemispatial Neglect
A clock drawn by a person with hemispatial neglect
• Reduced awareness on one side of the visual field
• Often occurs as a result of brain damage
• Not blindness • Patients may not know they
have hemispatial neglect
ConclusionContributionsBackground Study 2 Study 3Study 1
Hemispatial Neglect
12
Hemispatial Neglect
Eye movements of a patient with left-side hemispatial neglect. Patient was asked to search for letter T among Ls
• Reduced awareness on one side of the visual field
• Often occurs as a result of brain damage
• Not blindness • Patients may not know they
have hemispatial neglect
ConclusionContributionsBackground Study 2 Study 3Study 1
Hemispatial Neglect
13
ConclusionContributionsBackground Study 2 Study 3Study 1
Hemispatial Neglect
14
ConclusionContributionsBackground Study 2 Study 3Study 1
Hemispatial Neglect
HalvesLeft boy, girl, cookie, jar, stool, cupboard, steal, fall, kitchen
Right woman, exterior, sink, plate, dishcloth, water, window,
dishes, curtains, wash, overflow, cupboard, kitchen
StripsFar-left girl, cookie, jar, stool, cupboard, steal, kitchen, cupboard
Center-left boy, cookie, stool, steal, fall, kitchen, cupboard
Center-right woman, exterior, sink, plate, dishcloth, water, window,
dishes, curtains, wash, overflow, kitchen, cupboard
Far-right exterior, window, dishes, curtains, kitchen, cupboard
QuartersNE woman, exterior, plate, dishcloth, wash, window, curtains,
kitchen
SE woman, sink, water, dishes, overflow, cupboard, kitchen
NW girl, cookie, jar, cupboard, steal, boy, cookie, kitchen
SW girl, stool, fall, cupboard, kitchen
15
ConclusionContributionsBackground Study 2 Study 3Study 1
Hemispatial Neglect
attentioni = # of mentions of info-units in division i
concentrationi =# of mentions of info-units in division i
total words uttered
repetitioni =# of unique info-units mentioned in division i
# of mentions of info-units in division i
perceptioni =# of unique info-units mentioned in division i
# of infounits in division i
16
Discourse Features• In a coherent passage one sentence has a clear relation
to the next • Discourse parsing is the task of segmenting a piece of
text into EDUs* and then forming a discourse tree
* EDU = Elementary Discourse Unit
ConclusionContributionsBackground Study 2 Study 3Study 1 Discourse Analysis
17
Discourse tree for the two sentences: "But he added: 'Some people use the purchasers' index as a leading indicator, and some use it as a coincident
indicator. But the thing it's supposed to measure - manufacturing strength - is missed altogether last month.'"
ConclusionContributionsBackground Study 2 Study 3Study 1 Discourse Analysis
18
Discourse Features• # of each discourse relation • Depth of the discourse tree • Mean number of EDU per utterance • Discourse relation type-to-token ratio
ConclusionContributionsBackground Study 2 Study 3Study 1 Discourse Analysis
• Baseline: Fraser’s feature set • Psycholinguistic (5) • Vocabulary Richness (4): • Acoustic (172): • Info-Units (40) • Parts-of-speech (15) • Context-free-grammar rules (44) • Syntactic Complexity (27) • Repetitiveness (5)
• 10-fold CV • Feature selection preprocessing step
19
Methodology
A parse tree for “a child is stealing cookies”
ConclusionContributionsBackground Study 2 Study 3Study 1 Methodology
20
ConclusionContributionsBackground Study 2 Study 3Study 1 Results
• Halves improves best model by 2.2%
• Strips second largest improvement
• Quadrant and discourse features have negligible effect
halves -> hemispatial neglect features w/ halves partition strips -> hemispatial neglect features w/ strips partition quadrants -> hemispatial neglect features w/ quadrants partition
Results
21
ConclusionContributionsBackground Study 2 Study 3Study 1 Results
• Plot shows change is performance (from baseline)
• Quadratic terms don’t help all models
Results
halves -> hemispatial neglect features w/ halves partition strips -> hemispatial neglect features w/ strips partition quadrants -> hemispatial neglect features w/ quadrants partition
22
ConclusionContributionsBackground Study 2 Study 3Study 1 Results
• Plot shows change is performance (from baseline)
• Quadratic terms don’t help all models
Results
halves -> hemispatial neglect features w/ halves partition strips -> hemispatial neglect features w/ strips partition quadrants -> hemispatial neglect features w/ quadrants partition
23
ConclusionContributionsBackground Study 2 Study 3Study 1 Results
Results• Features are scored
based on correlation w/ diagnosis
• Score of 1.0 -> most correlated feature across all folds
• Perception: Rightside most important feature (more than age!)
24
ConclusionContributionsBackground Study 2 Study 3Study 1 Results
Results• Participants w/
dementia:• are less
perceptive on the right side of the image
25
ConclusionContributionsBackground Study 2 Study 3Study 1 Results
Results• Participants w/
dementia:• use more
personal pronouns
26
ConclusionContributionsBackground Study 2 Study 3Study 1 Results
Results• Participants w/
dementia:• use shorter
words
27
ConclusionContributionsBackground Study 2 Study 3Study 1 Results
Results• Participants w/
dementia:• are older
28
ConclusionContributionsBackground Study 2 Study 3Study 1 Conclusion
Conclusion• Hemispatial neglect features
improve our ability to detect AD • On DementiaBank dataset • More correlated w/ diagnosis than age • Try with/without quadratic cross
terms
• Main negative result: • Discourse features have no effect on
DementiaBank dataset • May be due to non-narrative structure
of response
29
ConclusionContributionsBackground Study 3Study 1
Study 2 Written Samples
Study 2
30
Written Samples: Background• Most work to date has been with spoken language
samples • Detecting impairment from writing is more
difficult • No test-specific features • Not constrained to single topic • Author can make revisions
• There will be lots of data in the future • As the “internet generation” grows older • Today only 34% of seniors use social media
• Can we detect dementia from written language?
ConclusionContributionsBackground Study 3Study 1 Study 2 Written Samples
31
Data set• We developed novel data set of blog posts
• Three written by people with dementia • Three written by caretakers of people with dementia
• Will allow for: • Longitudinal analysis • Comparison between pathology, demographics • Comparison between subtypes of dementia
• We perform preliminary analysis • Is automatic detection possible?
ConclusionContributionsBackground Study 3Study 1 Study 2 Data set
32
Data set
ConclusionContributionsBackground Study 3Study 1
Blog Name Posts Words Start Date Diagnosis Gender/Age
living-with-alzhiemers 344 263.03 (s=140.28) Sept 2006 AD M, 72 (approx)
creatingmemories 618 242.22 (s=169.42) Dec 2003 AD F, 61
parkblog-silverfox 692 393.21 (s=181.54) May 2009 Lewy Body M, 65
journeywithdementia 201 803.91 (s=548.34) Mar 2012 Control F, unknown
earlyonset 452 615.11 (s=206.72) Jan 2008 Control F, unknown
helpparentsagewell 498 227.12 (s=209.17) Sept 2009 Control F, unknown
Study 2 Data set
33
Methodology• Features
• Part-of-speech (15), psycholinguistic (5), syntactic complexity (27), repetitiveness (5), Vocabulary Richness (4), context-free-grammar (44)
• No info-unit feature or acoustic features • Include feature selection
• 9-fold CV • Test fold -> posts from 1 dementia blog, 1 control blog • Train fold -> posts from remaining 4 blogs
• Task: predict class of unseen blog post
ConclusionContributionsBackground Study 2 Methodology
Study 3Study 1
34
ConclusionContributionsBackground Study 3Study 1 Study 2 Results
• All models beat majority class baseline
• KNN/LogReg have best AUC
Results
35
ConclusionContributionsBackground Study 3Study 1 Study 2 Results
• Ablation analysis • Remove feature
group, measure change in performance
• All feature groups are important • Psycholinguistic
features most of all
Results
36
ConclusionContributionsBackground Study 3Study 1 Study 2 Results
Results• SUBTLWord score
most important feature • Measure of how
frequently a word is used in daily life
• Pronoun Use/ Word Length also score highly • Same findings as in
spoken dataset
37
ConclusionContributionsBackground Study 3Study 1 Study 2 Results
Results
• ↑ SUBTLWord score == ↓ Vocabulary • Red == dementia
• SUBTLWord score most important feature • Measure of how
frequently a word is used in daily life
• Pronoun Use/ Word Length also score highly • Same findings as in
spoken dataset
38
ConclusionContributionsBackground Study 3Study 1 Study 2 Conclusion
Conclusion• Despite difficulties associated with written language, it is
possible to detect dementia from blog posts • All feature groups significant • All models achieved AUC above baseline (.50) • Blogger w/ dementia use simpler language
• SUBTL word score
• Future work • Control for education level • Topic clustering preprocessing step • Longitudinal analysis
39
ConclusionContributionsBackground Study 2Study 1
Study 3 Domain
Adaptation
Study 3
40
Domain Adaptation: Background• Mild Cognitive Impairment
• Precursor stage which may lead to eventual dementia diagnosis
• Potentially treatable • Less data available than for AD
• Domain adaptation: • Useful when you have lots of data
from one domain and you want to use it to help you in a second domain
ConclusionContributionsBackground Study 2Study 1 Study 3 Domain
Adaptation
41
Methodology• Idea: Use AD data to improve MCI classification
accuracy • We compare two domain adaptation algorithms
against baselines: • Alg1: AUGMENT • Alg2: CORAL
• DementiaBank Data: • Source : 257 AD, 201 control • Target : 43 MCI samples, 41 control,
• 10-fold cross validation • Only target data in test fold
ConclusionContributionsBackground Study 2 Study 3 Methodology
Study 1
42
Alg1: AUGMENT• From widely cited 2009 paper “Frustratingly Easy
Domain Adaptation” by Hal Daumé III • Augment feature space by creating a shared, target
only, and source only copy of each feature:
ConclusionContributionsBackground Study 2 Study 3 Alg1: AUGMENT
Study 1
source data = Alzheimer’s + control, target data = Mild Cognitive Impairment + control
43
Alg1: AUGMENT
ConclusionContributionsBackground Study 2 Study 3 Alg1: AUGMENT
Study 1
source data = Alzheimer’s + control, target data = Mild Cognitive Impairment + control
• Imagine d=2
# of words mean word length
71 4.1
71 4.1
44
Alg1: AUGMENT
ConclusionContributionsBackground Study 2 Study 3 Alg1: AUGMENT
Study 1
source data = Alzheimer’s + control, target data = Mild Cognitive Impairment + control
• Imagine d=2
source (AD)
target (MCI)
71 4.1
69 6.1
64 2.0
73 3.1
60 3.3
65 7.1
71 4.1
69 6.1
64 2.0
73 3.1
60 3.3
65 7.1
45
Alg1: AUGMENT
ConclusionContributionsBackground Study 2 Study 3 Alg1: AUGMENT
Study 1
source data = Alzheimer’s + control, target data = Mild Cognitive Impairment + control
• Imagine d=2
71 4.1 0 0 71 4.1
69 6.1 0 0 69 6.1
64 2.0 0 0 64 2.0
73 3.1 0 0 73 3.1
60 3.3 60 3.3 0 0
65 7.1 65 7.1 0 0
source (AD)
target (MCI)
71 4.171 4.171 4.1
69 6.1
64 2.0
73 3.1
60 3.3
65 7.1
71 4.1
69 6.1
64 2.0
73 3.1
60 3.3
65 7.1
46
Alg1: AUGMENT
ConclusionContributionsBackground Study 2 Study 3 Alg1: AUGMENT
Study 1
source data = Alzheimer’s + control, target data = Mild Cognitive Impairment + control
• Imagine d=2shared
71 4.1
69 6.1
64 2.0
73 3.1
60 3.3
65 7.1
target only
source only{ { {
source (AD)
target (MCI)
71 4.1 0 0 71 4.1
69 6.1 0 0 69 6.1
64 2.0 0 0 64 2.0
73 3.1 0 0 73 3.1
60 3.3 60 3.3 0 0
65 7.1 65 7.1 0 0
71 4.1
69 6.1
64 2.0
73 3.1
60 3.3
65 7.1
47
Alg1: AUGMENT
ConclusionContributionsBackground Study 2 Study 3 Alg1: AUGMENT
Study 1
source data = Alzheimer’s + control, target data = Mild Cognitive Impairment + control
• Important cavet! • Model must learn a weight vector
for this to work • e.g., KNN, Naive Bayes not expected to improve
48
Alg2: CORAL (CORrelation ALignment)• “CORAL minimizes domain shift by aligning the
second-order statistics of source and target distributions*”
• How? Make covariance matrix of source data == covariance matrix of target data
*B. Sun, J. Feng, and K. Saenko. Return of frustratingly easy domain adaptation. arXiv preprint arXiv:1511.05547, 2015
ConclusionContributionsBackground Study 2 Study 3 Alg2: CORAL
Study 1
source data = Alzheimer’s + control, target data = Mild Cognitive Impairment + control
49
Alg2: CORAL (CORrelation ALignment)
ConclusionContributionsBackground Study 2 Study 3 Alg2: CORAL
Study 1
source data = Alzheimer’s + control, target data = Mild Cognitive Impairment + control
50
ConclusionContributionsBackground Study 2 Study 3 Results
Study 1
Results• Alg1: AUGMENT
improves upon all baselines
• 5% increase from target_only
• Alg2: CORAL result in a worse performance than target_only
source data = Alzheimer’s + control, target data = Mild Cognitive Impairment + control
51 source data = Alzheimer’s + control, target data = Mild Cognitive Impairment + control
ConclusionContributionsBackground Study 2Study 1 Study 3 Results
Results• Alg1: AUGMENT
fails to improve models that do not learn a weight vector • KNN • Random Forests • Naive Bayes
52AD = Alzheimer’s Disease, MCI = Mild Cognitive Impairment
ConclusionContributionsBackground Study 2Study 1 Study 3 Conclusion
Conclusion• Mild Cognitive Impairment is hard to detect:
• Lack of data • Less symptomatic
• We can improve MCI detection with AD data • Using domain adaptation
• Alg1: AUGMENT algorithm increases F-measure by 5% • Model must learn a weight vector
• Main negative result: • Alg2: CORAL doesn’t work on DementiaBank dataset • May be due to binary features
53
Motivation• Detecting dementia early is very
important • Key to finding a cure • For patients to receive support
• NLP + ML can be used to detect dementia from language samples
• Language samples are easy to collect • Application can distributed easily to
developing countries
Conclusion Motivation
ContributionsBackground Study 2 Study 3Study 1
“The Progression of Alzheimer's Through My Mom's Crocheting” - wuillermania
54
Contributions• We showed:
• Measuring hemispatial neglect improves accuracy
• It’s possible to automatically detect dementia from unstructured written text
• AD data can improve our ability to detect MCI using domain adaptation
Conclusion Contributions
ContributionsBackground Study 2 Study 3Study 1
“The Progression of Alzheimer's Through My Mom's Crocheting” - wuillermania
55
Contribution
Conclusion Summary
ContributionsBackground Study 2 Study 3Study 1
“The Progression of Alzheimer's Through My Mom's Crocheting” - wuillermania
• We released a new data set • Blog posts • Will permit longitudinal analysis
• Main negative results: • Discourse features have no effect w/
CookieTheft data • Domain adaptation alg2: CORAL
performs poorly w/ CookieTheft data
56
Future work
Conclusion Future work
ContributionsBackground Study 2 Study 3Study 1
“The Progression of Alzheimer's Through My Mom's Crocheting” - wuillermania
• Multiple source domains • AD, vascular dementia, dementia with
Lewy Bodies • CookieTheft, Narrative Retelling Task,
Blogs
• Extend written data set • Emails • Forum conversations • Presidential tweets
• Longitudinal analysis • Track changes in writing style as
disease progresses
57
Acknowledgements• Dr. Giuseppe Carenini, University of British Columbia • Dr. Gabriel Murray, University of Fraser Valley • Dr. Thalia Field, UBC Faculty of Medicine
58
Thank you!
59
Methodology Feature Selection
60
Contribution 1 Results
F-Measure Baseline Strips Halves Quarters DiscourseLogReg 0.824 (0.798-0.850) 0.833 (0.801-0.866) 0.846 (0.813-0.878) 0.821 (0.784-0.859) 0.824 (0.798-0.850)
SVM 0.737 (0.688-0.786) 0.725 (0.683-0.766) 0.721 (0.678-0.764) 0.730 (0.693-0.767) 0.737 (0.688-0.786)
KNN 0.692 (0.654-0.729) 0.716 (0.674-0.759) 0.728 (0.669-0.788) 0.707 (0.675-0.739) 0.692 (0.654-0.729)
RandomForest 0.796 (0.761-0.832) 0.802 (0.759-0.845) 0.754 (0.717-0.792) 0.799 (0.765-0.834) 0.800 (0.763-0.836)
NaiveBayes 0.780 (0.746-0.814) 0.777 (0.747-0.806) 0.760 (0.732-0.789) 0.780 (0.750-0.810) 0.780 (0.746-0.814)
61
Contribution 1 Results
Change in F-Measure Strips Halves Quarters DiscourseLogReg 0.010 (-0.008-0.027) 0.022 (0.003-0.041) -0.002 (-0.033-0.028) 0.000
SVM -0.012 (-0.058-0.034) -0.016 (-0.084-0.053) -0.007 (-0.034-0.020) 0.000
KNN 0.025 (-0.013-0.062) 0.037 (-0.025-0.099) 0.016 (-0.013-0.045) 0.000
RandomForest 0.006 (-0.026-0.037) -0.042 (-0.072--0.012) 0.003 (-0.011-0.017) 0.003 (-0.012-0.019)
NaiveBayes -0.004 (-0.017-0.010) -0.020 (-0.046-0.006) -0.000 (-0.008-0.007) 0.000
62
Contribution 1 Results
Change in F-Measure Strips Halves Quarters DiscourseLogReg 0.010 (-0.008-0.027) 0.004 (-0.024-0.033) -0.002 (-0.033-0.028) 0.000
SVM -0.012 (-0.058-0.034) -0.019 (-0.052-0.014) -0.007 (-0.034-0.020) 0.000
KNN 0.025 (-0.013-0.062) 0.022 (-0.010-0.054) 0.016 (-0.013-0.045) 0.000
RandomForest 0.006 (-0.026-0.037) 0.004 (-0.028-0.037) 0.003 (-0.011-0.017) 0.003 (-0.012-0.019)
NaiveBayes -0.004 (-0.017-0.010) 0.001 (-0.011-0.013) -0.000 (-0.008-0.007) 0.000
63
Contribution 3 Results
Performance Majority Class LogReg SVM KNN Random
Forests Naive Bayes
Accuracy 0.515 (0.471-0.560)
0.822 (0.795-0.848)
0.723 (0.674-0.772)
0.701 (0.654-0.749)
0.796 (0.759-0.833)
0.782 (0.746-0.818)
AUC 0.500 0.894 (0.867-0.921)
0.769 (0.741-0.796)
0.750 (0.712-0.787)
0.871 (0.838-0.903)
0.846 (0.810-0.882)
F-Measure 0.677 (0.638-0.716)
0.824 (0.798-0.850)
0.737 (0.688-0.786)
0.692 (0.654-0.729)
0.796 (0.761-0.832)
0.780 (0.746-0.814)
64
Contribution 2 Results
F-Measure target only source only relabelled AUGMENT CORAL
Majority Class 0.283 (.122-0.445) 0.637 (.487-0.786) 0.637 (.487-0.786) 0.637 (.487-0.786) 0.637 (.487-0.786)
LogReg 0.667 (.505-0.829) 0.614 (.445-0.783) 0.690 (.577-0.803) 0.717 (.562-0.871) 0.637 (.487-0.786)
SVM 0.595 (.472-0.719) 0.637 (.487-0.786) 0.659 (.520-0.798) 0.664 (.533-0.796) 0.637 (.487-0.786)
source data = alzheimer’s + control, target data = MCI + control
65
Contribution 2 Results
F-Measure target only source only relabelled AUGMENT CORAL
Majority Class 0.283 (0.122-0.445) 0.637 (0.487-0.786) 0.637 (0.487-0.786) 0.637 (0.487-0.786) 0.637 (0.487-0.786)
RandomForest 0.602 (0.503-0.701) 0.670 (0.534-0.806) 0.695 (0.558-0.832) 0.581 (0.478-0.684) 0.642 (0.490-0.794)
KNN 0.597 (0.429-0.764) 0.522 (0.347-0.696) 0.550 (0.372-0.729) 0.558 (0.357-0.760) 0.637 (0.487-0.786)
NaiveBayes 0.536 (0.362-0.710) 0.520 (0.396-0.643) 0.545 (0.422-0.668) 0.512 (0.425-0.599) 0.647 (0.502-0.791)
source data = alzheimer’s + control, target data = MCI + control
66
Contribution 2 Domain Adaptation: CORAL
Whitening
source data = alzheimer’s + control, target data = MCI + control
67
Contribution 2 Domain Adaptation: CORAL
Recolouring
source data = alzheimer’s + control, target data = MCI + control
68
Contribution 3 Results
Performance Majority Class LogReg SVM KNN Random
Forests Naive Bayes
Accuracy 0.629 (0.583-0.674)
0.724 (0.677-0.770)
0.638 (0.595-0.681)
0.728 (0.687-0.769)
0.681 (0.617-0.745)
0.658 (0.595-0.721)
AUC 0.500 0.759 (0.689-0.829)
0.666 (0.613-0.719)
0.761 (0.714-0.807)
0.696 (0.599-0.792)
0.674 (0.574-0.775)
F-Measure 0.770 (0.736-0.804)
0.785 (0.743-0.827)
0.773 (0.739-0.806)
0.785 (0.746-0.823)
0.766 (0.719-0.813)
0.732 (0.680-0.783)