Autism Spectrum Disorder Prediction
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 Data preprocessing — Cleaned and prepared ASD screening datasets with feature engineering Model development — Implemented SVM and CNN architectures for binary classification Evaluation — Compared model performance using accuracy, precision, recall, and F1-score 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. ...