ComfortADHD: A Stacking Ensemble Framework for Early ADHD Prediction Integrating Machine Learning, Explainable AI, LLMs, and Dialogflow-Assisted Virtual Therapy Chatbot

ComfortADHD is an end-to-end framework for early ADHD prediction, utilizing a Stacking Ensemble of ML models for high accuracy. It features XAI for clinical transparency, LLMs for behavioral analysis, and a Dialogflow-assisted chatbot for virtual therapy, bridging the gap between screening and patient support.The extracted dataset has been uploaded in this repo.To avail the whole dataset, the following link should be approached: https://www.childhealthdata.org/dataset Citation: Child and Adolescent Health Measurement Initiative (CAHMI) [(Publication Year)]. [Year and name of survey], [(SAS/SPSS/Stata)] Indicator dataset. Data Resource Center for Child and Adolescent Health supported by Cooperative Agreement from the U.S. Department of Health and Human Services, Health Resources and Services Administration (HRSA), Maternal and Child Health Bureau (MCHB). Retrieved [mm/dd/yy] from www.childhealthdata.org