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Understanding Low Back Pain using
Fuzzy Association Rule Mining
Dr Maybin Muyeba
(Co-authors: Sandra Liewis, Liangxiu Han and John A. Keane)
Big Data Research Seminar , MMU 2013
Outline of the Presentation
Organised as follows:
 Background
 The problem
 Technical Solution
 Data Preprocessing
 Experiments
 Conclusion
Big Data Research Seminar , MMU 2013
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Background
 Low
Back Pain (LBP) is widespread in the UK
 One (1) in four(4) adults consult medical attention
 Reported to be largest cause of absence from work
 Symptoms range from minimal impairment to
severe disability
 Psychosocial factors have influence on LBP
 Fear of movement, depression, self-efficacy,
catastrophising, anxiety, pain duration, defensiveness, pain
levels, muscle activity (EMG) etc
Big Data Research Seminar , MMU 2013
The Problem
 Poor
Correlation of Psychosocial factors
and physical damage to the spine
 Numerical measurements imprecise
 Likert,
HADS scales
 Less
than 15% receive specific
diagnosis
 Identification
of subgroups of LBP patients
 Specific interventions
Big Data Research Seminar , MMU 2013
The Problem – HADS, RASCH
scales
Hospital Anxiety and Depression scale (HADS) has 7
items, each rated 0 to 3 scale
 Determining extent of individual feeling
   e.g. “I feel tense or wound up” (Catastrophising)
e.g. “no pain” to “worst possible pain” (Pain levels)
RASCH measurement used to enhance HADS
reliability and validity
 PROBLEM: How to capture linguistic (fuzzy)
expressions meaningfully and appropriately, and
represent diagnosis in human terms
 PROBLEM: How to find (correlations) between these
fuzzy expressions
 Big Data Research Seminar , MMU 2013
Technical solution 1
 Fuzziness
and Correlation, a perfect fit for
Fuzzy Correlation or Fuzzy Association
Rule Mining (FARM)
 FARM - to identify correlations between
physical and psychosocial factors
 FARM - to assist better understanding of
how psychosocial factors affect LBP
Big Data Research Seminar , MMU 2013
Technical solution 2:Big Data Mining
 Data Mining
  Patterns:
    Large

Dimensionality

Imprecise (Fuzzy) 
Size, memory issues, Parr Processors
No. features, most irrelevant, some similar
no clear boundary, Computing words
Challenges with big Data
     Anything that can be termed “interesting” and is beyond a database
query or simple statistical analysis
Challenges with big Data
  The extraction of implicit, previously unknown and potentially useful
patterns from large data.
Distributed

different locations, Parr Processors
Heterogeneous (Image, Web, transactions, EOS, legacy.. etc)
Streaming (vs static) 
memory, Parr Processors
Efficient algorithms to cope with the six (6) challenges and more
Effective algorithms to report what is interesting
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Imprecise data (Fuzzy modelling)
  A set with a fuzzy boundary = fuzzy set
Most real world data is fuzzy e.g. high pain, highly infected wound,
small bone cracks, few joint movements
A = Set of Pain levels
Fuzzy set A
Crisp set A
1.0
1.0
.9
Membership
.5
function
6
0
Fuzzy set
Pain level
10
0
Membership
function
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Seminar , MMU 2013
(MF)
4
8
Pain level
Universe or
universe of discourse
Technical solution – 3 Fuzzy
Association rule
 A Fuzzy Association
rule is an implication of the
form
where
items
 e.g.
are fuzzy sets,
and
Big Data Research Seminar , MMU 2013
are disjoint
Technical Solution 4 – Fuzzy
support, Confidence, Correlation
 Fuzzy Support (FS) – degree of membership of items (e.g.
symptoms) in the set of transactions
,and,
where
 e.g. FS(“PainLevel),
 Fuzzy Confidence (FC) 
 Fuzzy Correlation (FCorr) 
Fuzzy rules are chosen by filtering out those with more positive correlation values
(closer to 1) than others.
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Data Preprocessing 1 - KL Measure
Measuring irregularly distributed data
Redundant observations, leads to unnecessary
computational costs
 Solution: measure information content of input features
and remove redundant ones
 Kullback-leibler (KL) – measures relative entropy
.9
distance between two probability
density functions ,
.5
and
  Smaller KL values indicate little divergence i.e. similar information
content between the distributions while large values indicate
diverse content (a lot more information).
Big Data Research Seminar , MMU 2013
Data Preprocessing 2 – Information
Content
Removed
attribute (‡)
Attr #
Attribute (Feature)
KL
Sign. Order
1 (‡)
Height
0.0364
(10)
2
Age
0.0414
(8)
3
Weight
0.0896
(1)
4
Pain duration(yrs)
0.0389
(9)
5 (‡)
Pain levels (0 to 10)
0.0100
15
6
Disability
0.0269
11
7
Anxiety
0.0132
14
8
Depression
0.0100
15
9
Self-efficacy
0.0523
4
10
Pain related anxiety
0.0640
(3)
11
Fear of movement
0.0466
(6)
12
Catastrophising
0.0441
(7)
13
Defensiveness
0.0047
16
14
Stature
0.0670
(2)
15
Muscle activity
0.0364
(10)
16
Pain anxiety fear
0.0495
(5)
17 (‡)
Pain escape avoidance
0.0206
Big Data Research Seminar , MMU 2013
Pain anxiety physiological
0.0315
18
13
12
Similar KL
Values,
Experiments – Frequent itemsets, rules
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Experiments – Fuzzy Rules 1
Rule#
Rule description
R1
Age=medium depression=low
Fcorr=:0.875
Age=medium, pain-anxiety-fear=highdepression=low,
Fcorr=:0.80
R2
R3
Age=medium, catastroph-rumination=highdepression=low , Fcorr=:0.80
R4
Age=medium, muscleactivity=mediumweight=medium, depression=low ,
Fcorr=:0.721
R5
depression=low, catastroph-rumination=medium Age=medium pain-anxiety-fear=high
Fcorr=:0.671
fuzzy rules (), 17 attributes)
Big Data Research Seminar , MMU 2013
Experiments – Fuzzy Rules 2
Rule #
Rule description
R1
Anxiety-low depression-low
R2
Age=medium, anxiety=low  depression=low
Fcorr=0.381
R3
Pain-anxiety-avoid=low  depression-low
Fcorr=0.185
R4
muscleactivity=medium depression=low
Fcorr=0.132
R5
Anxiety=low muscleactivity=medium  depression=low, pain-anxiety-avoid=low
Fcorr=0.421
R6
Age=medium  depression=low
Fcorr=0.470
Fcorr= -0.064
fuzzy rules (), 22 attributes
Big Data Research Seminar , MMU 2013
Experiments – Expert comments 3
Rule#, Table#
R1, -T4
R2, -T4
R3, -T4
Expert comment
Result in line with research which shows that depression reaches its lowest level in the middle aged.
Result is an outlier as low back pain research has typically shown a positive correlation between fear of
pain and depression
Result is an outlier since research has been consistent in demonstrating that rumination (a subscale of
catastrophizing which is characterised by a repetitive and passive focus on one’s negative emotions) is
positively correlated with depression. In fact, rumination has been found to maintain and exacerbate
depressed mood and predict elevated levels of depressive symptoms
R4,- T4
Results in line with population data which shows that, on average, weight generally increases with age
(more rapidly before age 30). It is unknown how normalised muscle activity varies with weight. In
fact, one of the main reasons to normalise the muscle activity data is to remove the confounding effect
of weight.
R5, -T4
Depression is thought to reach its lowest level in the middle aged and research suggests a negative link
between catastrophising and age and a positive correlation between catastrophising and pain-related
fear. Therefore these results are all broadly in line with previous research. However the results
between depression and pain related fear are an outlier as prior research has shown a positive
correlation between these two factors
R1,R2,R3,R4,R6,-T5
Consistent with previous comments
R5, T5
Results consistent with established knowledge which has repeatedly found a close correlation
between anxiety and depression. In particular, they often co-occur within clinical populations
Big Data Research Seminar , MMU 2013
Experiments – Expert Summary
 From
clinical perspective, fuzzy rules
generated are easier to interpret and
understand, with the results being
consistent with various studies
 Results indicate known associations
between various psychosocial and
physical factors for LBP
 Over 85% of the generated fuzzy
association rules are consistent
Big Data Research Seminar , MMU 2013
Conclusion
 Chronic
back pain – with features of the data
(anxiety, depression, defensiveness etc) has been
studied
 Correlations and subscales of the data yields fuzzy
expressions and fuzzy associations
 FARM algorithm with feature selection was applied
to a real dataset
 ** Improvements in fuzzy partitioning e.g. Ruspinitype, C4.5-type etc
 ** Bigger dataset to verify fuzzy association
inferences
Big Data Research Seminar , MMU 2013
References
    Muyeba, M., Han, L. and Keane, J. A. “Understanding Low Back Pain using
Fuzzy Association Rule Mining”, (to appear), IEEE System, Man and
Cybernetics workshop on Issues in EHR representation, integration, analysis,
Manchester, 2013
Lewis, P. Holmes, S. Woby, J. Hindle, N. Fowler. “The relationships between
measures of stature recovery, muscle activity and psychological factors in
patients with chronic low back pain”, Manual Therapy, 2012;17(1):27–33
[10] M. Muyeba, M. S. Khan, F. Coenen: “A Framework for Mining Fuzzy
Association Rules from Composite Items”, PAKDD Workshops 2008: 62-74
Hestbaek L., Leboeuf Y.C., Manniche C. “Low back pain: what is the longterm course? A review of studies of general patient populations”. Eur Spine J.
2003;2:149–165
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