Paper PublishingDeep Analysis of Autism Spectrum Disorder Detection Techniques.
Objective
The aim of this project is to find out the most significant traits and automate the diagnosis of Autism Spectrum Disorder using available machine learning classification techniques for improved diagnosis purpose. At final, we compare accuracy of various machine learning algorithms for early autism detection.
Abstract
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. In present day Autism Spectrum Disorder (ASD) is gaining its momentum faster than ever. Detecting autism traits through screening tests is very expensive and time consuming.
With the advancement of artificial intelligence and machine learning (ML), autism can be predicted at quite early stage. The main aim of this project is to analyze various Machine learning algorithms, used by various researcher like SVM (support Vector Machine), Random Forest, Decision Trees, Logistic Regression and compare the result based on their accuracy and efficiency.
Keywords: Machine Learning, SVM, Classifier, Genetic.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
HARDWARE SPECIFICATIONS:
- Processor: I3/Intel
- Processor RAM: 4GB (min)
- Hard Disk: 128 GB
- Key Board: Standard Windows Keyboard
- Mouse: Two or Three Button Mouse
- Monitor: Any
SOFTWARE SPECIFICATIONS:
- Operating System: Windows 7+
- Server-side Script: Python 3.6+
- IDE: PyCharm
- Libraries Used: Pandas, Numpy, sklearn, Flask.
Learning Outcomes
- Uses of Unsupervised Learning.
- Importance of classification.
- Scope of Autism detection.
- Use of Decision Trees techniques.
- Importance of PyCharm IDE.
- How ensemble models works.
- Working of Support Vector Machines.
- Process of debugging a code.
- Input and Output modules
- How test the project based on user inputs and observe the output
- Project Development Skills:
- Problem analyzing skills.
- Problem solving skills.
- Creativity and imaginary skills.
- Programming skills.
- Deployment.
- Testing skills.
- Debugging skills.
- Project presentation skills.
- Thesis writing skills.
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Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
