Predicting Autism in Toddlers through Machine Learning
Challenge Background
Nearly 1% of the population globally sufferers from Autism. Autism is a lifelong battle and early detection of autism can cure or help to manage the condition better. Therefore, a autism prediction model can help both child and parents to have better quality of life.
The Problem
Current screening processes incur costs and require expertise. Therefore, this project will apply ML to screen autism in children using a simple set of parameters reducing the cost and expertise needed.
Goal of the Project
- Exploratory Data Analysis
- Train ML model to predict Autism.
- Use explainability to identify the reason behind predictions.
- Write a research paper.
Project Timeline
Exploratory Data Analysis
Train Classical ML Models
Train Deep Learning ML Models
Analyze results including Explainability and bias analysis
Write a research paper based on the results
Write a research paper based on the results
What you'll learn
EDA, Machine Learning, Deep Learning, Explainable AI, Writing Research Papers
First Omdena Local Chapter Project?
Beginner-friendly, but also welcomes experts
Education-focused
Duration: 4 to 8 weeks
Open-source
Your Benefits
Address a significant real-world problem with your skills
Build your project portfolio
Access paid projects (as an Omdena Top Talent)
Get hired at top organizations
Requirements
Good English
Suitable for AI/ Data Science beginners but also more senior collaborators
Learning mindset
Application Form
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