York University
Instructor: Professor N.Cercone
Noada Lugaj
Jason Panas
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Introduction
Medical Diagnosis Background
Challenges and Problem Definition
Past Work
Decision Theory
Artificial Neural Network
Our Approach and Algorithm Description
Limitations
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Medical Diagnosis is one of the hardest fields of
medicine.
and
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Automating the process
In this project we aim to investigate the application
of artificial neural networks in medical diagnosis
and propose a simple and applied method for that.
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• Medical Diagnosis Background
Medical Diagnosis has been always seen as art. Throughout the history
we remember famous doctors as well as famous painters or composers
Hippocrates (c. 460–370 BCE) — Greek father of medicine
Elliott P. Joslin (1869–1962) — pioneer in the treatment of diabetes
Theodor Kocher thyroid surgery and first surgeon to win the Nobel Prize
It is undeniable the contribution of these doctors and many other famous
physicians for their role in the advancement of medicine.
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Answer
Question
Diagnosis and a
treatment
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Education to
become a doctor
requires long time
and is expensive
A sufficient number of
experienced cases only in the
middle of his career.
Performance of a
doctor can be
degraded from fatigue
and emotional
situation
More complications for new
diseases where experienced
physicians are in the same
position as newcomers.
A good diagnosis
Humans resemble pattern
recognition systems and
not statistic computers
= a talented and
experienced
physician
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The situation is fairly complicated and includes many problems so it would
be nice if computers could help.
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Computers have served well the medical sector for a few decades now.
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They have been used widely in medicine and patient databases: local and
global; digital archives and emergency networks.
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It is unrealistic to expect a fully automated computer based medical
diagnosis system because of the complexity of this task. But it is quite
obvious why a system for automated medical diagnosis would enhance
medical care and reduce costs.
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• Decision Theory
Medical Diagnosis is a vital task that should be performed as
accurately and efficiently possible.
Decision Theory can be used in order to implement a computer based medical
diagnosis system.
In mathematics and statistics Decision theory is concerned with identifying the
values, uncertainties and other issues relevant in a given decision, its rationality,
and the resulting optimal decision. Decision theory can be used to make optimal
choices based on probabilities and utilities. On one hand probability theory tells
us the probability of future states and how to represent uncertainty events. On
the other hand utility theory values different possible events so they can be
compared to each other. Decision theory can be used during information
gathering in a diagnosis session to determine which new evidence will be most
efficient to acquire next or which further evidence will no longer improve the
accuracy of diagnosis .Hence Decision theory can be used to decide what to do
next in order to maximize the information gathered or when to stop gathering
information.
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• Artificial Neural Network(ANN)
Neural Network is a very popular topic in
Artificial Intelligent field.
ANN are designed to simulate the behavior of
biological neural networks for several
purposes.
It is an attempt to simulate within specialized
hardware or sophisticated software, the
multiple layers of simple processing elements
called neurons. Each neuron is linked to
certain of its neighbors with varying coefficients
of connectivity that represent the strengths of
these connections. Learning is accomplished
by adjusting these strengths to cause the
overall network to output appropriate results.
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Neural Networks are ideal in recognizing diseases since there is no need to
provide a specific algorithm on how to identify the disease.
ANN is highly able to derive meaning from complicated and even imprecise
data. It can be used to recognize patterns or trends that are almost impossible
to be detected by humans or other computer-based techniques.
One of the most important problems of medical diagnosis, in general, is to
perform pattern recognition activities. This is when ANN comes to help.
A study in 1971 showed these basic facts in the medical area. This study
had shown that human have many limitations in diagnosis. The results of this
experiment were as follows:
• Best human diagnosis (most experienced physician): 79.7%
• Computer with expert data base: 82.2%
• Computer with 600 patient data: 91.1%
From these results we can see that humans cannot analyze complex data
without errors.
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Neural Network approach has been used in diagnosing diseases using patient
medical data such as breast cancer, heart failure, medical images, acidosis
diseases, and lung cancer.
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Breast cancer
It is the second largest cause of cancer deaths among women. The automatic
diagnosis of breast cancer is an important, real-world medical problem.
This has given rise, over the past few decades, to computerized diagnostic
tools, intended to aid the physician in making sense out of the confusion of
data. General Regression Neural Network and Probabilistic Neural Network
can be effectively used for breast cancer diagnosis to help oncologists.
Hence, ANN has been proven of their capabilities in many domains such
as medical application. Neural network with ability to learn by example
makes them very flexible and powerful in medical diagnosis.
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 Programmed in Java
 Algorithm uses 1 linked list which holds custom objects
called “disease”
 Disease objects are composed of 3 parts:
Disease name
LinkedList of symptoms
Integer “match” variable
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The combination of these 2 data structures allows us to use a neural
network kind of approach to this problem.
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With each symptom that is listed, that symptom is run through the linkedlist
of diseases, and the match variable of each disease is updated.
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After each object in our knowledge base is checked and matched, then
each item that has a match is returned to the user with a weight that is the
probability that that disease is the one the patient will have.
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Weighting is done as follows:
 A disease has a symptom listed multiple times if it is a more prominent
symptom.
 Once a symptom is given, the match level is increased by the number
of times the symptom appears.
 When all the symptoms that have a match we get the highest match #
of the number of symptoms given, whichever is higher.
 For each match a disease doesn’t have, the % for the disease is
reduced by a factor of 2n, where n is the difference in match values,
with the rest being distributed over the other diseases of higher match
levels.
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Demo
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The program aims to show the implementation of ANN in medical
diagnosis. It does not focus on how to setup proper medical data. This is
why the results produced do not have any medical meaning or are
irrelevant to any real life medical diagnosis process. But it shows how
important and how accurately the process of Medical Diagnosis can be
done using ANN techniques.
In order to get more medically accurate results it would require to collect real
data from real cases and maybe add a more elaborated manipulation process
upon this data.
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Thank you
November,29,2011
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