Accurate Identification of ADHD among Adults Using Real-Time Activity Data

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopment disorder that affects millions of children and typically persists into adulthood. It must be diagnosed efficiently and consistently to receive adequate treatment, otherwise, it can have a detrimental impact on the patient’s professional performance, mental health, and relationships. In this work, motor activity data of adults suffering from ADHD and clinical controls has been preprocessed to obtain 788 activity-related statistical features. Afterwards, principal component analysis has been carried out to obtain significant features for accurate classification. These features are then fed into six different machine learning algorithms for classification, which include C4.5, kNN, Random Forest, LogitBoost, SVM, and Naive Bayes. The detailed evaluation of the results through 10-fold cross-validation reveals that SVM outperforms other classifiers with an accuracy of 98.43%, F-measure of 98.42%, sensitivity of 98.33%, specificity of 98.56% and AUC of 0.983. Thus, a PCA-based SVM approach appears to be an effective choice for accurate identification of ADHD patients among other clinical controls using real-time analysis of activity data.

The EPIDIA4Kids protocol for a digital epidemiology study on brain functioning in children, based on a multimodality biometry tool running on an unmodified tablet

“…Importantly, e-screening through a careful gamification can address the most prominent barriers to screening identified by children, relatives, and healthcare providers. It would also ease the acceptability and feasibility in real life practice and provide personalization to patient needs (23) with real-time data (24)(25)(26)(27) along anonymity (28)(29)(30). Recently, digital biomarkers have shown a strong potential to predict mental issues in adults (31)(32)(33), but none has investigated in children whether biometry could be relevant for a better understanding of neurodevelopmental conditions and for the identification potential subtypes as a NDD continuum in children (34).…”