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Prediction of Hepatitis C using Artificial Neural Network

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Title: Prediction of Hepatitis C using Artificial Neural Network


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Prediction of Hepatitis C using Artificial
Neural Network
  • Rinki Jajoo, Dinesh Mital, Syed Haque

2
  • SUMMARY - RESEARCH
  • The main objective of this research project is
    develop an expert systems system for the
    diagnosis of Hepatitis C, based on a back
    propagation feed forward artificial neural
    network (ANN),
  • Compare its performance with other existing
    computer based decision support systems.
  • The ANN based system is developed with the
    commercially available software package (Brain
    Maker, California scientific Software).
  • Two different types of ANN models, unsupervised
    and supervised, are developed, compared, and
    tested.
  • The predictive accuracy and the training for the
    supervised model is significantly better.
  • The model is able to predict the Hepatitis C in
    patients accurately.

3
  • The performance of the proposed ANN model is
    not significantly better than the traditional
    computer model based techniques.
  • Further investigations are needed to understand
    the impact of this methodology on the outcome
    analysis.
  • An existing database of Hepatitis C infected
    patients is used. Data of 15 infected and 20
    non-infected individuals are used in the model.
    This could be one of the possible reasons that
    the results were not significantly better.
  • Continuous variable are recorded for patient
    age, ethnicity, and patient number. The results
    have been very interesting, however, some further
    research work is required to fine-tune the
    results.
  • The main advantage of the developed system is
    that it is adaptive and self- learning type.

4
  • ARTIFICIAL NEURAL NETWORKS
  • Artificial Neural networks are composed of a
    series of computational nodes structured into
    several layers.
  • Back propagation feed-forward network is used for
    this application.
  • Each node is connected to all nodes in the
    previous layer.
  • The Artificial Neural network structure used for
    in this project consists of a three layered feed
    forward design.

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Neuron Structure
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  • The first layer is an input layer in which each
    node represent an input variable. The second
    layer is a hidden computational layer and the
    third layer is a single output node representing
    the outcome for a given data record. (i.e.,
    Hepatitis C)
  • The weighted sum of the input data ( nodes) is
    calculated and presented to each node in the
    hidden layer.
  • A transfer function then takes the weighted sum
    and calculates a value that numerically scales
    the strength of each nodes output.
  • The output of each succeeding layer becomes the
    input of the next layer.

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  • There are two user-dependent parameter that
    affects the training.
  • a) The learning rate and
  • b) The momentum.
  • The learning rate defines the amount by which
    weights are changed during each iteration. A
    larger learning rate leads to larger weight
    change.
  • Momentum allows the weight change to be
    proportional to the previous weight change.

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  • THE MODEL
  • The Artificial Neural networks in our study used
    a sigmoid transfer function and the network was
    trained with Feed forward-back propagation
    learning algorithm.
  • The structure of the network is a three layered
    with one hidden layer.
  • During training, the outcome is known for each
    input data record. That is system is trained on
    the known patient data taken from the database.
  • After the training is completed, the system is
    validated on the known patient database.
  • During the training, the output value for the
    Artificial Neural network is calculated, the
    value is compared with the known output from the
    training data.

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  • If there is a discrepancy between the calculated
    and the actual output, the error is propagated
    through the Artificial Neural network and weights
    are adjusted until the error discrepancy is
    within the allowed tolerance.
  • The outcome of the proposed model shows that the
    supervised model is better than unsupervised
    system.
  • The training of the network is done using a
    number of facts (input data).

12
  • There are 8 hidden neurons used by the hidden
    layer of the model.
  • In each training run there are multiple number of
    inputs (facts) to be trained.
  • When the system is trained using the available
    facts it tries to learn after each iteration.This
    system was trained using 1218 facts in 87
    training runs. There were 14 facts in each run.
  • During the training period, the RMS error
    decreased (0.079).
  • When system started learning there are no good
    output(s). All the outputs are bad. After the
    extensive training, there should be no bad
    outputs - most of the outputs will be good.

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  • CONCLUSION
  • We have designed, implemented and tested two
    different artificial neural network models.
  • Both the models are tested with normal and
    infected patient data.
  • The classification performance of the Supervised
    ANN model is superior than either rule based
    model or Unsupervised network.
  • The ANN based systems are self adaptive and learn
    with each additional input data set provided to
    it.

21
  • CONCLUSION ( contd. )
  • Currently, the models have been tested on a very
    limited and small patient database. However, we
    plan to test it rigorously on large patient
    database.
  • We believe that this proposed model for
    predicting the Hepatitis C would assist doctors
    in the diagnosis of disease.
  • Accuracy of the model for the prediction of test
    cases is over 80 for the supervised network.

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  • Learning
  • An ANN learns from its experience. The usual
    process of learning involves three tasks
  • Compute output(s).
  • Compare outputs with desired results and feedback
    the error.
  • Adjust the weights and repeat the process.
  • The learning process starts by setting the
    weights by some rules ( or randomly). The
    difference between the actual output (y) and the
    desired output(z) is called error (delta).
  • The objective is to minimize delta ( to zero).
    The reduction in delta is done by changing the
    weights

23
  • Supervised
  • Uses a set of inputs for which the desired
    outputs results / classes are known.The
    difference between the desired and actual output
    is used to calculate adjustment to weights of the
    NN structure.
  • Unsupervised
  • Only input stimuli (parameters) are presented to
    the network. The network is self organizing,
  • No knowledge is supplied about the classification
    of outputs. However, the number of categories
    into which the network classifies the inputs can
    be controlled by varying certain parameters in
    the model.

24
  • Developing NN Applications
  • Important step is the selection of network
    structureThe available network structures are
  • Associative Memory Systems
  • It refers to ability to recall complete
    situations from partial information. Such systems
    correlate input data with information stored in
    memory,
  • Information can be recalled even from incomplete
    or noisy inputs.
  • Associative memory systems can detect
    similarities between new inputs stored input
    patterns.
  • Hidden Layer Systems
  • Complex practical applications require one or
    more hidden layers between inputs and outputs and
    and a corresponding large number of weights.

25
Inputs Each input corresponds to a single
attribute. For example for diagnosing a disease,
each symptom, could represent an input to one
node. Input could be image of skin texture, if
we are looking for cancer cells. Outputs The
outputs of the network represent the solution to
a problem. For diagnosis of a disease, the answer
could be yes or no. Weights A key element of
ANN is weight. Weight expresses relative strength
of the entering data from various connections
that transfers data from input point to the
output point.
26
The Procedure for developing NN applications is
1. Collect Data. 2. Separate the data into
training and test sets. 3. Define the network
structure. 4. Select a learning
algorithm. 5. Transform data to network
inputs. 6. Start training and revise weights
until the error criterion is satisfied. 7. Stop
and test the results with test data. 8. Implementa
tion use network for the new case. Transformatio
n Function One of the common function is sigmoid
function where YT is the transformed value of Y.
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