Overview

Designed classification models for Autism Spectrum Disorder (ASD) detection using Support Vector Machines and Convolutional Neural Networks, achieving 94% accuracy. This research was published in the International Journal of Information Technology and Computer Science (IJITCS).

Publication

Published in: International Journal of Information Technology and Computer Science (IJITCS), Vol.14, No.4

Approach

  1. Data preprocessing — Cleaned and prepared ASD screening datasets with feature engineering
  2. Model development — Implemented SVM and CNN architectures for binary classification
  3. Evaluation — Compared model performance using accuracy, precision, recall, and F1-score
  4. Visualization — Created Tableau dashboards to help healthcare professionals understand demographic patterns

Tech Stack

  • Python (Scikit-learn, TensorFlow)
  • Support Vector Machines
  • Convolutional Neural Networks
  • Tableau

Results

  • 94% accuracy on ASD detection
  • CNN outperformed traditional ML approaches on this dataset
  • Visualization dashboards provided actionable insights for healthcare professionals

Key Takeaway

This project combined rigorous ML methodology with healthcare domain application, demonstrating how deep learning can assist in early screening for developmental conditions.