J-Eros is a machine learning algorithm for computing similarity between two multivariate time series along with k-Nearest-Neighbor classifier, to classify healthy vs ADHD children using just fMRI data without using any other data or demographics. We applied this technique to the public data provided by ADHD-200 Consortium competition and our results show that J-Eros is capable of discriminating healthy from ADHD children such that we outperformed other state of the art techniques. This machine learning algorithm is a major step towards diagnosing ADHD using quantitative methods and will be an essential part for diagnosing mental illnesses.
Status
The method development for this work is complete