automate the diagnosis process

1. OBJECTIVE
The focus of this project is to find out the most significant traits and automate
the diagnosis process using available classification techniques for improved
diagnosis purpose.
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2. Autism Spectrum Disorder (ASD), which is a neuro development disorder, is
often accompanied by sensory issues such an over sensitivity or under
sensitivity to sounds and smells or touch. Although its main cause is genetics
in nature, early detection and treatment can help to improve the conditions.
The project gives a new approach for identification of ASD using a deep
classifier.Deep Learning (DL) has become a common technique for the early
diagnosis of AD. Here, we introduce prediction on Autism Disorder using
Recurrent Neural Network help researchers diagnose the disease at its early
stages. The ASD identification works in the following steps. Feature analysis
explains ASD traits thereby improving the efficiency of screening process.
Further, Machine Learning (ML) classifier models report ASD class type with
evaluation parameters. With this the accuracy and performance of the model is
increased.
ABSTRACT

3. INTRODUCTION
• Autism spectrum disorder (ASD), is a neurological developmental disorder.
It affects how people communicate and interact with others, as well as how
they behave and learn.
• The symptoms and signs appear when a child is very young.
• It is a lifelong condition and cannot be completely cured.
• A study found that 33% of children with difficulties other than ASD have
some ASD symptoms while not meeting the full classification criteria.
• ASD has a significant economic impact both due to the increase in the
number of ASD cases worldwide, and the time and costs involved in
diagnosing a patient.
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4. LITERATURE SURVEY
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TITLE AUTHOR and Year Pros Cons DATASET METRICES
A Survey of Machine
Learning Based Approaches
for Autism Disease Prediction
Shubham Bind1
2021
Used Realtime
MRI Images
Less Accuracy MRI Dataset 75
Classification of Autism’s
disease and its stages using
machine learning
John
Michael Templeton
2022
active MRI
patches are
extracted
The training
database consist less
image
MRI2D 65
Analysis and Prediction of
Autism's Disease using
Machine Learning
Algorithms
Sohom Sen
2022
Used 30 MRI
Images
The system was
trained with Less
type of Disease.
BRAINMRI 84
Detection of Autism Disease
Using Machine Learning
G.Priyadharshini,
T.Gowtham
2020
High Accuracy lowest accuracy with
40%.
MRIDATA 90
Predicting Severity Of
Autism's Disease Using Deep
Learning
Srishti Grover
2020
Dataset
belonging to
Multiple
category
Accuracy Low database with
Disease by Name
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5. Existing system
• Several studies have made use of machine learning in various ways to
improve and speed up the diagnosis of ASD.
• Soft computing techniques such as probabilistic reasoning, artificial neural
networks (ANN), and classifier combination have also been used .
• Artificial Neural Network was used to classify between patients with and
without ASD, respectively which gave lesser accuracy.

6. PROBLEM DEFINITION
• The system gives comparatively lower results which is approximately
around 93%.
• Artificial Neural Network leads to loss of data and consumes high amount
of data which leads to lesser accuracy.
• Moreover, it was also observed that different areas in a scan or an image
had different textural and morphological patterns which were difficult for
human beings to track.
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7. PROPOSED SYSTEM
• The combination of Data-driven approach and Rule-based approach is
employed for the development of the model.
• We begin by preprocessing the dataset to eliminate missing values and
outliers, remove noise, and encode categorical attributes.
• We also employ feature engineering to choose the most beneficial features
out of all the features present in the data set.
• This reduces data dimensionality to improve speed and efficiency during
training. Once the data set has been preprocessed, classification algorithm
used was Recurrent Neural Network.
• RNN predict the output label (ASD or no ASD). The accuracy of each
classifier is observed and compared.
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8. PROPOSED SYSTEM
• Furthermore, metrics like the F1 score and precision-recall values have also
been computed for better evaluation of each classifier.
• If the classifier performs well, then the training accuracy will be higher
than its test accuracy.
• This model can then be deemed to be the best model and hence be used for
further training and classification.

9. SYSTEM ARCHITECTURE
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10. CLASSIFIER
• A Recurrent Neural Network (RNN) is a Deep Learning algorithm which
can take in an input image, assign importance (learnable weights and
biases) to various aspects/objects in the image and be able to differentiate
one from the other.
• The pre-processing required in a RNN is much lower as compared to other
classification algorithms.
• While in primitive methods filters are hand-engineered, with enough
training, RNN have the ability to learn these filters/characteristics.
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11. DATASET
• Abstract: Autism EEG Disease Detection Dataset
• Data Set Characteristics: Multivariate
• Number of Instances: 197
• Area: Life
• Attribute Characteristics: Real
• Number of Attributes: 23
• Associated Tasks: Classification
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12. ADVANTAGES
• They recognized faster and more accurately .
• The model is trained to learn the low level to high level features and the
classification results are validated.
• This system consumes lesser memory so that the processing time is
reduced.
• The data loss is considerably lower.
• Thus with a rapid growth in the Machine learning architectures, an
objective diagnosis of Autism’s disease will no longer be a laborious job for
the clinicians in the near future.
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13. SYSTEM REQUIREMENTS
Software Description Version
Anaconda
Software used for
computer vision with
Machine Learning
4.2.0
Language - Python
Programming language
used to program the
Project
3.7

14. CONCLUSION
• In this project, we have analyzed the ASD datasets of toddler, child,
adolescent and adult. We apply most popular five feature selection methods
to derive fewer features from ASD datasets yet maintaining competitive
performance.
• We find that EEG feature selection method outperforms amongst others.
• In our experimental setup, we increase the attribute numbers gradually and
then apply different classification techniques.
• We find that MLP outperforms amongst all other classifiers using our
methodology and approach.

15. REFERENCES
[1] C.-W. Tsai, R.-T. Tsai, S.-P. Liu, C.-S. Chen, M.-C. Tsai, S.-H. Chien, H.-S.
Hung, S.-Z. Lin, W.-C. Shyu, and R.-H. Fu, “Neuroprotective effects of betulin
in pharmacological and transgenic caenorhabditis elegans models of Autism
disease,” Cell Transplantation, vol. 26, no. 12, pp. 1903–1918, 2017.
[2] D. Frosini, M. Cosottini, D. Volterrani, and R. Ceravolo, “Neuroimaging in
Autism’s disease: Focus on substantia nigra and nigro-striatal projection,”
Current Opinion in Neurology, vol. 30, no. 4, pp. 416–426, 2017.
[3] K. Marek, D. Jennings, S. Lasch, A. Siderowf, C. Tanner, T. Simuni, C.
Coffey, K. Kieburtz, E. Flagg, S. Chowdhury et al., “The Autism progression
marker initiative (ppmi),” Progress in Neurobiology, vol. 95, no. 4, pp. 629–
635, 2017.
[4] J. Shi, Z. Xue, Y. Dai, B. Peng, Y. Dong, Q. Zhang, and Y. Zhang,
“Cascaded multi-column rvfl+ classifier for single-modal neuroimagingbased
diagnosis of Autism’s disease,” IEEE Transactions on Biomedical Engineering,
2018.
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