This document discusses data mining techniques and their applications in healthcare. It begins with an introduction to data mining, describing it as the process of identifying patterns in data. Several data mining techniques are covered, including classification models for prediction. The document then focuses on how these techniques can be used in healthcare for applications like disease diagnosis and treatment recommendations. Examples of data mining aiding healthcare include predicting patient health outcomes and uncovering specialized treatment paths for diseases.

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2. DATA MINING IN HEALTHCARE
Dr. V. Subha, B.E., M.E., Ph.D.,
Assistant Professor,
Department of Computer Science & Engineering,
Manonmaniam Sundaranar University,
Tirunelveli. 2

3. CONTENTS
1. Introduction to Data Mining
2. Data Mining techniques
3. Data Mining in Healthcare
4. Data Mining Resources
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4. 1. INTRODUCTION TO DATA MINING
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6. 6

7. Data Vs Knowledge
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10. Definition of Data Mining
• Process of identifying valid, novel,
potentially useful, and ultimately
understandable patterns in data.
- Predicts outcomes of future
observations.
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11. Confluence of different disciplines
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12. Steps in Data Mining Evolution
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13. Data Mining - Motivation
• Growth in data.
• High dimensionality of data.
• Heterogeneous and complex data.
• Development of commercial data mining software.
• Growth of computing power and storage capacity.
• Limitation of humans.
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14. Why Data Mining ?
• Credit ratings :
• Given a database of 100,000 names, which persons are the least likely
to default on their credit cards?
• Fraud detection :
• Which types of transactions are likely to be fraudulent, given the
demographics and transactional history of a particular customer?
• Customer relationship management:
• Which of my customers are likely to be the most loyal, and which are
most likely to leave for a competitor?
Data Mining helps extract such information
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15. Data Mining for decision making
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16. Types of data analysis
Predictive models help in diagnosing diseases and then
uncovering a special path for treatment.
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17. Knowledge Discovery in Databases (KDD)
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18. Steps of KDD Process
Data relevant to the
analysis is decided and
retrieved from the
database
Data cleansing -
Noisy data and
missing data are
handled
appropriately
Processed data is
transformed into
appropriate forms
for mining
Clever
techniques are
applied to
extract patterns
that are
potentially
useful
Mined data
patterns are
evaluated
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19. Applications of data mining
Popular applications of data mining 19

20. 2. DATA MINING TECHNIQUES
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21. Data Mining Techniques
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22. Data Mining Techniques…
• Unsupervised learning
- No knowledge of output
- Self-guided learning algorithm.
• Supervised learning
- Knowledge of output
- Learning with an expert/teacher
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23. Data Mining Techniques…
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24. Classification – Prediction Model
Data Mining
Algorithms
Classification
Prediction
• Process of finding a model that describes the data classes or concepts.
• Purpose is to use this model to predict the class of instances whose class
label is unknown.
• This derived model is based on the analysis of sets of training data.
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25. Example Dataset
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26. Dataset Splitting
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Model Construction
Training
Data
AGE GENDER CHEST PAIN DISEASE
63 Male atyp_angina yes
67 Male asympt yes
67 Male asympt yes
37 Male non_anginal no
41 Female atyp_angina no
62 Female asympt yes
Classification
Algorithms
IF chest pain = ‘asympt’
OR age > 53
THEN disease = ‘yes’
Classifier
(Model)

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Using the Model in Prediction
Classifier
(Model)
Testing Data
(50,male,asympt)
Disease?

29. Classification Techniques
Neural Networks Bayesian Networks
Decision Trees Support Vector Machines
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30. Confusion Matrix
• Contains information about actual and predicted classifications
done by a classification system.
• Performance of such systems is commonly evaluated using the
data in the confusion matrix.
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31. Confusion Matrix…
• TP – Patient with heart disease.
• FP – Patient wrongly identified to have heart disease.
• FN – Patient have been left out from treatment for heart disease.
• TN – Patient does not have heart disease.
Predicted
Class
Actual Class
Positive Negative
Positive True Positive (TP) False Positive (FP)
Negative False Negative (FN) True Negative (TN)
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32. Performance Metrics
• Accuracy =
• Sensitivity =
• Specificity =










FN
FP
TN
TP
TN
TP






 FN
TP
TP






 FP
TN
TN
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33. 3. DATA MINING IN HEALTHCARE
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34. Data mining in healthcare
• Hospitals deal with lot of data on a day to day basis.
• Difficult for a single person to handle.
• Data mining techniques help a lot in analyzing
patient’s health data.
• Patient’s health care data can be sorted and any kind
of illness can be predicted which helps in treating the
patients.
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35. Disease diagnosis and effective treatment
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36. Healthcare applications with Data mining
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37. Disease diagnosis using Data Mining
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38. 4. DATA MINING - RESOURCES
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39. Data mining Tools - Open Source
DataMelt
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40. Data mining Tools - Proprietary License
Oracle Data Mining
IBM Cognos IBM SPSS Modeler
SAS Data Mining Sisense
SSDT
Teradata Board toolkit Dundas BI
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41. Data Mining Tutorials
www.tutorialpoint.com
www.guru99.com
www.tutorialride.com
www.zentut.com
www.cs.cmu.edu
www.javatpoint.com
 www.analyticsvidhya.com
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42. Dataset Repository - UCI Repository
https://archive.ics.uci.edu
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43. Books
• Han, Jiawei, Jian Pei, and Micheline Kamber. Data
mining: concepts and techniques. Elsevier, 2011.
• Zaki, Mohammed J., Wagner Meira Jr, and Wagner
Meira. Data mining and analysis: Fundamental
concepts and algorithms. Cambridge University Press,
2014.
• Gorunescu, Florin. Data Mining: Concepts, models and
techniques. Vol. 12. Springer Science & Business
Media, 2011.
• Ron Zacharski. A Programmer's Guide to Data Mining
: The Ancient Art of the Numerati,2013
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