in the fields of decision-making and medical informatics

1. Presented by: Mohammad Reza Afrash
PhD Student of Medical Informatics,
Department of Medical & health management, School of Medicine,
Shahid beheshti University of Medical Sciences
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Decision trees
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2. Introduction
 What will I talking about ?
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Demo In SPSS

3. Introduction
 Introduction To Machine Learning :
Machine learning is a the subfield of computer science that provides computers with the ability to learn
without being explicitly programmed. - Arthur Samuel 1959.
Goal : Computers can learn the pattern in data automatically by using machine learning techniques
Training data
Learn
algorithm
Build model Perform
feedback

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 Introduction To Machine Learning :
Types of Learning
1- Supervised learning
* dependent and independent variables
* training process
2- Unsupervised learning
Without dependent variable
example: population clustering

5. Introduction
 Machine Learning example :
 Amazon has huge amount of customers purchasing
data.
 The data consist of costumer demographics (age, sex,
location), purchasing history, past browsing history.
 Based on this data, amazon segment its customers,
draw a pattern and recommends the right product to the
right customer at the right time
Machine Learning example :
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6. Introduction
 Introduction To Classification
 Classification is the problem of identifying to
which set of categories a new observation.
 It is a supervised learning model as the classifier
already has a set of classified examples and from
these example, the classifier learns to assign
unseen new examples.
 Example: assigning a given email into “spam” or
“none-spam” category.

7. Introduction
 Classification example :
Feed the classifier with training data set and predefined labels.
It will learn to categorize particular data under a specific label.

8. Introduction
 Classification use case :
Banking
Identification of loan risk application by their
probability of defaulting payments.
Medicine
Identification of at-risk patients and disease trends.
Remote sensing
Identification of areas of similar land use in GIs
data base.
Marketing
Identification of customer chum.

9. Introduction
 Type of Classifiers :
Naïve Bays
It is a classification technique
based on Bayes theorem with
an assumption of
independence among
attribute.
Decision tree
Decision tree builds classification
model in the form of a tree structure.
it breaks down a dataset into smaller
and smaller subsets.
Random forest
Random forest is an ensemble
classifier made using many
decision tree models.

10. Introduction
 What Is Decision Trees ?
Method of organizing decisions over time in the face of
uncertainties.
A decision tree uses a tree structure to specify sequences of
decision and consequences
A decision tree employee a structure of nodes and branches.
The depth of node is the minimum number of steps required to
reach the node from the root.
Eventually, a final point is reached and prediction is made.
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11. How Decision Trees works?
example:
‫بیمار‬66‫دارد‬ ‫مشکل‬ ‫شدت‬ ‫به‬ ‫زانو‬ ‫ناحیه‬ ‫از‬ ‫ای‬ ‫ساله‬.‫اس‬ ‫آمفیزم‬ ‫او‬ ‫دیگر‬ ‫مشکل‬‫ت‬
‫شود‬ ‫می‬ ‫نفس‬ ‫تنگی‬ ‫دچار‬ ‫که‬.‫ش‬ ‫فلج‬ ‫تقریبا‬ ‫سکته‬ ‫علت‬ ‫به‬ ‫وی‬ ‫همسر‬ ‫اخیرا‬‫است‬ ‫ده‬
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12.  How Decision Trees works?
‫گیری‬ ‫تصمیم‬ ‫درخت‬ ‫از‬ ‫استفاده‬ ‫با‬ ‫گیری‬ ‫تصمیم‬ ‫مراحل‬:
1-‫تصمیم‬ ‫درخت‬ ‫ایجاد‬
2-‫حاالت‬ ‫از‬ ‫یک‬ ‫هر‬ ‫برای‬ ‫انتظار‬ ‫مورد‬ ‫ارزش‬ ‫ی‬ ‫محاسبه‬
3-‫انتظار‬ ‫مورد‬ ‫ارزش‬ ‫بیشترین‬ ‫با‬ ‫تصمیم‬ ‫انتخاب‬
4-‫نتایج‬ ‫آزمایش‬ ‫برای‬ ‫حساسیت‬ ‫تحلیل‬ ‫از‬ ‫استفاده‬
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13. Introduction
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14.  How Decision Trees works?
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15.  How Decision Trees works?

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