Project Overview
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social interaction, communication, and repetitive behaviors. Early detection and intervention are crucial for optimal outcomes. This project aims to develop a machine learning model to detect autism based on eye-tracking data.
Project Goals
Collect and preprocess eye-tracking data from autistic and neurotypical individuals.
Extract relevant features from the eye-tracking data.
Develop and train machine learning models to classify individuals as autistic or neurotypical.
Evaluate the performance of the models using appropriate metrics.
Data Collection
Participants: Recruit a diverse group of participants, including autistic and neurotypical individuals of varying ages.
Eye-Tracking Equipment: Use high-quality eye-tracking devices to record participants' eye movements.
Stimuli: Present visual stimuli designed to elicit specific eye movement patterns.
Data Preprocessing
Data Cleaning: Remove noise and artifacts from the eye-tracking data.
Feature Extraction: Extract relevant features such as fixation duration, saccade amplitude, and scan paths.
Data Normalization: Normalize the data to ensure consistent scales.
Model Development and Training
Algorithm Selection: Choose appropriate machine learning algorithms (e.g., SVM, Random Forest, Neural Networks).
Model Training: Train the models using the extracted features and labeled data.
Hyperparameter Tuning: Optimize model performance through hyperparameter tuning.
Model Evaluation
Performance Metrics: Use metrics like accuracy, precision, recall, and F1-score to evaluate the model's performance.
Cross-Validation: Perform cross-validation to assess model generalization.
Ethical Considerations
Data Privacy: Ensure the privacy and confidentiality of participant data.
Bias: Address potential biases in the dataset and model development process.
Clinical Validation: Collaborate with medical professionals for validation and clinical application.
Potential Challenges
Data Collection: Obtaining sufficient high-quality eye-tracking data can be challenging.
Feature Engineering: Extracting meaningful features from eye-tracking data requires domain expertise.
Model Interpretability: Understanding the decision-making process of the model is crucial.
Note: This project requires collaboration with medical professionals, ethical approval, and adherence to data privacy regulations.
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