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Dental Data Mining:
Practical Issues
and
Potential Pitfalls
Stuart A. Gansky
University of California, San Francisco
Center to Address Disparities in Children’s Oral Health
Support: US DHHS/NIH/NIDCR U54 DE14251
What is Knowledge Discovery
and Data Mining (KDD)?
• “Semi-automatic discovery of patterns, associations,
anomalies, and statistically significant structures in data”
– MIT Tech Review (2001)
• Interface of
– Artificial Intelligence
– Computer Science
– Machine Language
– Engineering
– Statistics
• Association for Computing Machinery Special Interest
Group on Knowledge Discovery in Data and Data
Mining (ACM SIGKDD sponsors KDD Cup)
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Data Mining as Alchemy
Pb
Au
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Some Potential KDD Applications
in Oral Health Research
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Large surveys (eg NHANES)
Longitudinal studies (eg VA Aging Study)
Disease registries (eg SEER)
Digital diagnostics (radiographic & others)
Molecular biology (eg PCR, microarrays)
Health services research / claims data
Provider and workforce databases
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Supervised Learning
Unsupervised Learning
• Regression
• Hierarchical clustering
• k nearest neighbor
• k-means
• Trees (CART, MART,
boosting, bagging)
• Random Forests
• Multivariate Adaptive
Regression Splines (MARS)
• Neural Networks
• Support Vector Machines
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KDD Steps
Collect
& Store
PreProcess
Sample
Merge
Warehouse
Clean
Impute
Transform
Standardize
Register
Analyze
Supervised
Unsupervised
Visualize
Validate
Act
Internal
Intervene
Split Sample
Set Policy
Cross-validate
Bootstrap
External
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Data Quality
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Example – Caries
• Predicting disease with traditional logistic
regression may have modelling difficulties:
nonlinearity (ANN better) & interactions (CART
better)(Kattan et al, Comp Biomed Res, ’98)
• Want to compare the performance of logistic
regression to popular data mining techniques –
tree and artificial neural network models in
dental caries data
• CART in caries (Stewart & Stamm, JDR, ’91)
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Example study – child caries
• Background: ~20% of children have ~80% of
caries (tooth decay)
• University of Rochester longitudinal study
(Leverett et al, J Dent Res, 1993)
• 466 1st-2nd graders caries-free at baseline
• Saliva samples & exams every 6 months
• Goal: Predict 24 month caries incidence (output)
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18-month Predictors (Inputs)
• Salivary bacteria
– Mutans Streptococci (log10 CFU/ml)
– Lactobacilli (log10 CFU/ml)
• Salivary chemistry
– Fluoride (ppm)
– Calcium (mmol/l)
– Phosphate (ppm)
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Modeling Methods
Logistic
Regression
Neural
Networks
Decision
Trees
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Logistic Regression Models
Logit (Primary Dentition Caries)
Schematic Surface
Fluoride (F) ppm
log10 Mutans
Streptococci
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Tree Models
Logit (Primary Dentition Caries)
Schematic Surface
Fluoride (F) ppm
log10 Mutans
Streptococci
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Artificial Neural Networks
Logit (Primary Dentition Caries)
Schematic Surface
Fluoride (F) ppm
log10 Mutans
Streptococci
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Artificial Neural Network (p-r-1)
wij
x1
h1
x2
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xp
inputs
wj
h2
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y
hr
hidden layer (neurons)
output
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Common Mistakes with ANN
(Scwartzer et al, StatMed, 2000)
• Too many parameters for sample size
• No validation
• No model complexity penalty
(eg Akaike Information Criterion (AIC))
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Incorrect misclassification estimation
Implausible function
Incorrectly described network complexity
Inadequate statistical competitors
Insufficiently compared to stat competitors
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Validation
• Split sample (70% training/30% validation)
Validation estimates unbiased misclassification
• K-fold Cross Validation
Mean squared error (Brier Score)
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Why Validate?
Example: Overfitting in 2 Dimensions
Data
Response
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Predictor
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Linear Fit to Data
Response
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y = 0.3449x + 1.2802
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R = 0.9081
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Predictor
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High Degree Polynomial Fit to Data
Response
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y = -0.0012x + 0.1196x - 4.8889x + 105.05x - 1250.4x + 7811.5x - 19989
R2 = 1
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Predictor
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10-Fold Cross-validation
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10-Fold Cross-validation
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10-Fold Cross-validation
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Caries Example
Model Settings
• Logit
– Stepwise selection
– Alpha=.05 to enter, alpha=.20 to stay
– AIC to judge additional predictors
• Tree
– Splitting criterion: Gini index
– Pruning: Proportion correctly classified
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ANN Settings
• Artifical Neural Network (5-3-1 = 22 df)
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Multilayer perceptron
5 Preliminary runs
Levenberg-Marquardt optimization
No weight decay parameter
Average error selection
3 Hidden nodes/neurons
Activation function: hyperbolic tangent
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ANN Sensitivity Analyses
• Random seeds: 5 values
– No differences
• Weight decay parameters: 0, .001, .005, .01, .25
– Only slight differences for .01 and .25
• Hidden nodes/neurons: 2, 3, 4
– 3 seems best
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Tree Model
N=322 Training
N=144 Validation
Overall
Primary Caries
15%
log10 MS <7.08
15%
log10 LB <3.05
10%
log10 MS <3.91
3%
Prevalence: Node > Overall (15%)
Prevalence: Node < Overall (15%)
log10 MS 7.08
91%
log10 LB 3.05
23%
log10 MS 3.91
14%
F < .056
22%
F < .110
100%
F  .110
0%
F  .056
25%
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Receiver Operating Characteristic
(ROC) Curves
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Cumulative Captured Response Curves
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Lift Chart
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Logistic Regression
Beta Std Err Odds Ratio 95% CI
log10 MS .238 .072
1.27
1.10 – 1.46
log10 LB .311
.070
1.36
1.19 – 1.57
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MARS – MS at 4 Times
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Predicted Quintiles
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Rank for Variable PR_ANN
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Predicted Quintiles
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Rank for Variable PR_ANN
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5-fold CV Results
RMS error
AUC
Logit
.365
.680
Tree
.363
.553
ANN
.362
.707
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Summary
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Data quality and study design are paramount
Utilize multiple methods
Be sure to validate
Graphical displays help interpretations
KDD methods may provide advantages over
traditional statistical models in dental data
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Prediction
as good as the
data
and
model
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