Elucidating upon the mystical stigmas of ADHD

The core agenda of this research paper was an in-depth study and analysis of attention deficit hyperactivity disorder (ADHD), which is a developmental disorder. The study includes finding the root causes of ADHD and studying about the same to get a profound idea about the disorder and then further using machine learning models to predict ADHD in an individual. We initiated our study by trying to figure out several diagnostic processes that are involved or available for the treatment of ADHD and then taking up a dataset to implement exploratory data analysis on the available dataset which involved the cleaning, preprocessing and other parameters of analysis on the dataset. Further, we implemented several machine learning models such as XGBoost and Random Forest to train our model and test for the accuracy to infer the results through the probability scores generated by our machine learning model, indicating the likelihood that an individual exhibits ADHD characteristics. We also carried out a literature survey that helped us in drawing valuable comparisons which in turn gave us some valuable input through which we could improve the accuracy of our model and present a much better model in contrast to those that already exist.