Introduction to Neural Networks in Medical Diagnosis

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Introduction to Neural Networks in Medical
Diagnosis

• Wlodzislaw Duch
• Dept. of Informatics, Nicholas Copernicus
  University, Torun, Poland

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What is it about?

• Data is precious! But also overwhelming ...
• Statistical methods are important but new
  techniques may frequently be more accurate and
  give more insight into the data.
• Data analysis requires intelligence.
• Inspirations come from many sources, including
  biology artificial neural networks, evolutionary
  computing, immune systems ...

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Computational Intelligence
Computational IntelligenceData
KnowledgeArtificial Intelligence
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What do these methods do?

• Provide non-parametric models of data.
• Allow to classify new data to pre-defined
  categories, supporting diagnosis prognosis.
• Allow to discover new categories.
• Allow to understand the data, creating fuzzy or
  crisp logical rules.
• Help to visualize multi-dimensional relationships
  among data samples.
• Help to model real neural networks!

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GhostMiner Philosophy

• GhostMiner, data mining tools from our lab.
• Separate the process of model building and
  knowledge discovery from model use gt
  GhostMiner Developer GhostMiner Analyzer

• There is no free lunch provide different type
  of tools for knowledge discovery. Decision tree,
  neural, neurofuzzy, similarity-based, committees.
• Provide tools for visualization of data.
• Support the process of knowledge discovery/model
  building and evaluating, organizing it into
  projects.

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Neural networks

• Inspired by neurobiology simple elements
  cooperate changing internal parameters.
• Large field, dozens of different models, over 500
  papers on NN in medicine each year.
• Supervised networks heteroassociative mapping
  XgtY, symptoms gt diseases,universal
  approximators.
• Unsupervised networks clusterization,
  competitive learning, autoassociation.
• Reinforcement learning modeling behavior,
  playing games, sequential data.

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Supervised learning

• Compare the desired with the achieved outputs
  you cant always get what you want.

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Unsupervised learning

• Find interesting structures in data.

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Reinforcement learning

• Reward comes after the sequence of actions.

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Real and artificial neurons
Nodes artificial neurons
Dendrites
Signals
Synapses
Synapses
(weights)
Axon
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Neural network for MI diagnosis
Myocardial Infarction
p(MIX)
0.7
Outputweights
Inputweights
Sex
Age
Smoking
Elevation
Pain
ECG ST
Duration
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MI network function

• Training setting the values of weights and
  thresholds, efficient algorithms exist.

Effect non-linear regression function
Such networks are universal approximators they
may learn any mapping X gt Y
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Learning dynamics
Decision regions shown every 200 training epochs
in x3, x4 coordinates borders are optimally
placed with wide margins.
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Neurofuzzy systems
Fuzzy m(x)0,1 (no/yes) replaced by a degree
m(x)?0,1. Triangular, trapezoidal, Gaussian ...
MF.
M.f-s in many dimensions

• Feature Space Mapping (FSM) neurofuzzy system.
• Neural adaptation, estimation of probability
  density distribution (PDF) using single hidden
  layer network (RBF-like) with nodes realizing
  separable functions

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Knowledge from networks

• Simplify networks force most weights to 0,
  quantize remaining parameters, be constructive!

• Regularization mathematical technique
  improving predictive abilities of the network.
• Result MLP2LN neural networks that are
  equivalent to logical rules.

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MLP2LN

• Converts MLP neural networks into a network
  performing logical operations (LN).

Input layer
Output one node per class.
Aggregation better features
Rule units threshold logic
Linguistic units windows, filters
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Recurrence of breast cancer

• Data from Institute of Oncology, University
  Medical Center, Ljubljana, Yugoslavia.

286 cases, 201 no recurrence (70.3), 85
recurrence cases (29.7) no-recurrence-events,
40-49, premeno, 25-29, 0-2, ?, 2, left,
right_low, yes 9 nominal features age (9 bins),
menopause, tumor-size (12 bins), nodes involved
(13 bins), node-caps, degree-malignant (1,2,3),
breast, breast quad, radiation.
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Recurrence of breast cancer

• Data from Institute of Oncology, University
  Medical Center, Ljubljana, Yugoslavia.

Many systems used, 65-78 accuracy reported.
Single rule IF (nodes-involved ? 0,2 Ù
degree-malignant 3 THEN recurrence, ELSE
no-recurrence 76.2 accuracy, only trivial
knowledge in the data Highly malignant breast
cancer involving many nodes is likely to strike
back.
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Recurrence - comparison.
Method 10xCV accuracy MLP2LN 1
rule 76.2 SSV DT stable rules 75.7 ? 1.0
k-NN, k10, Canberra 74.1 ?1.2 MLPbackprop.
73.5 ? 9.4 (Zarndt)CART DT 71.4 ? 5.0
(Zarndt) FSM, Gaussian nodes 71.7 ? 6.8 Naive
Bayes 69.3 ? 10.0 (Zarndt) Other decision
trees lt 70.0
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Breast cancer diagnosis.

• Data from University of Wisconsin Hospital,
  Madison, collected by dr. W.H. Wolberg.

699 cases, 9 features quantized from 1 to 10
clump thickness, uniformity of cell size,
uniformity of cell shape, marginal adhesion,
single epithelial cell size, bare nuclei, bland
chromatin, normal nucleoli, mitoses Tasks
distinguish benign from malignant cases.
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Breast cancer rules.

• Data from University of Wisconsin Hospital,
  Madison, collected by dr. W.H. Wolberg.

Simplest rule from MLP2LN, large regularization
If uniformity of cell size lt 3 Then
benign Else malignant Sensitivity0.97,
Specificity0.85 More complex NN solutions, from
10CV estimate Sensitivity 0.98,
Specificity0.94
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Breast cancer comparison.
Method 10xCV accuracy k-NN, k3,
Manh 97.0 ? 2.1 (GM)FSM, neurofuzzy 96.9 ?
1.4 (GM) Fisher LDA 96.8 MLPbackprop.
96.7 (Ster, Dobnikar)LVQ 96.6 (Ster,
Dobnikar) IncNet (neural) 96.4 ? 2.1 (GM)Naive
Bayes 96.4 SSV DT, 3 crisp rules 96.0 ?
2.9 (GM) LDA (linear discriminant) 96.0
Various decision trees 93.5-95.6
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Melanoma skin cancer

• Collected in the Outpatient Center of Dermatology
  in Rzeszów, Poland.
• Four types of Melanoma benign, blue, suspicious,
  or malignant.

• 250 cases, with almost equal class distribution.
• Each record in the database has 13 attributes
  asymmetry, border, color (6), diversity (5).
• TDS (Total Dermatoscopy Score) - single index
• Goal hardware scanner for preliminary diagnosis.

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Melanoma results
Method Rules Training Test MLP2LN,
crisp rules 4 98.0 all 100 SSV Tree,
crisp rules 4 97.50.3 100FSM,
rectangular f. 7 95.51.0 100 knn
prototype selection 13 97.50.0 100
FSM, Gaussian f. 15 93.71.0 953.6 knn
k1, Manh, 2 features -- 97.40.3 100 LERS,
rough rules 21 -- 96.2
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Antibiotic activity of pyrimidine compounds.
Pyrimidines which compound has stronger
antibiotic activity?
Common template, substitutions added at 3
positions, R3, R4 and R5.
27 features taken into account polarity, size,
hydrogen-bond donor or acceptor, pi-donor or
acceptor, polarizability, sigma effect. Pairs of
chemicals, 54 features, are compared, which one
has higher activity? 2788 cases, 5-fold
crossvalidation tests.
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Antibiotic activity - results.
Pyrimidines which compound has stronger
antibiotic activity?
Mean Spearman's rank correlation coefficient
used -1lt rs lt 1 Method Rank correlation
FSM, 41 Gaussian rules 0.770.03Golem
(ILP) 0.68Linear regression 0.65CART
(decision tree) 0.50
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Thyroid screening.

• Garavan Institute, Sydney, Australia
• 15 binary, 6 continuous
• Training 931913488 Validate 731773178
• Determine important clinical factors
• Calculate prob. of each diagnosis.

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Thyroid some results.
Accuracy of diagnoses obtained with different
systems.
Method Rules/Features Training
Test MLP2LN optimized 4/6 99.9
99.36 CART/SSV Decision Trees 3/5
99.8 99.33 Best Backprop MLP
-/21 100 98.5 Naïve Bayes -/-
97.0 96.1 k-nearest neighbors -/-
- 93.8
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Psychometry

• MMPI (Minnesota Multiphasic Personality
  Inventory) psychometric test.
• Printed forms are scanned or computerized version
  of the test is used.

• Raw data 550 questions, exI am getting tired
  quickly Yes - Dont know - No
• Results are combined into 10 clinical scales and
  4 validity scales using fixed coefficients.
• Each scale measures tendencies towards
  hypochondria, schizophrenia, psychopathic
  deviations, depression, hysteria, paranoia etc.

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Scanned form
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Computer input
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Scales
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Psychometry

• There is no simple correlation between single
  values and final diagnosis.
• Results are displayed in form of a histogram,
  called a psychogram. Interpretation depends on
  the experience and skill of an expert, takes into
  account correlations between peaks.

Goal an expert system providing evaluation and
interpretation of MMPI tests at an expert level.
Problem agreement between experts only 70 of
the time alternative diagnosis and personality
changes over time are important.
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Psychogram
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Psychometric data

• 1600 cases for woman, same number for men.
• 27 classes norm, psychopathic, schizophrenia,
  paranoia, neurosis, mania, simulation,
  alcoholism, drug addiction, criminal tendencies,
  abnormal behavior due to ...

Extraction of logical rules 14 scales
features. Define linguistic variables and use
FSM, MLP2LN, SSV - giving about 2-3 rules/class.
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Psychometric data
Method Data N. rules Accuracy Gx
C 4.5 ? 55 93.0 93.7
? 61 92.5 93.1
FSM ? 69 95.4 97.6
? 98 95.9 96.9
10-CV for FSM is 82-85, for C4.5 is 79-84.
Input uncertainty Gx around 1.5 (best ROC)
improves FSM results to 90-92.
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Psychometric Expert

• Probabilities for different classes. For greater
  uncertainties more classes are predicted.
• Fitting the rules to the conditions
• typically 3-5 conditions per rule, Gaussian
  distributions around measured values that fall
  into the rule interval are shown in green.
• Verbal interpretation of each case, rule and
  scale dependent.

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MMPI probabilities
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MMPI rules
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MMPI verbal comments
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Visualization

• Probability of classes versus input uncertainty.
• Detailed input probabilities around the measured
  values vs. change in the single scale changes
  over time define patients trajectory.
• Interactive multidimensional scaling zooming on
  the new case to inspect its similarity to other
  cases.

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Class probability/uncertainty
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Class probability/feature
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MDS visualization
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Summary

• Neural networks and other computational
  intelligence methods are useful additions to the
  multivariate statistical tools.
• They support diagnosis, predictions, and data
  understanding extracting rules, prototypes.

FDA has approved many devices that use
ANNs Oxfords Instruments Ltd EEG analyzer,
Cardionetics (UK) ECG analyzer. PAPNET (NSI),
analysis of Pap smears
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Challenges

• Fully automatic universal data analysis systems
  press the button and wait for the truth

• Discovery of theories rather than data models
• Integration with image/signal analysis
• Integration with reasoning in complex domains
• Combining expert systems with neural networks
• .

We are slowly getting there. More more
computational intelligence tools (including our
own) are available.