  In recent years the importance of Smart Healthcare cannot be overstated. The
current work proposed to expand the state-of-art of smart healthcare in
integrating solutions for Obsessive Compulsive Disorder (OCD). Identification
of OCD from oxidative stress biomarkers (OSBs) using machine learning is an
important development in the study of OCD. However, this process involves the
collection of OCD class labels from hospitals, collection of corresponding OSBs
from biochemical laboratories, integrated and labeled dataset creation, use of
suitable machine learning algorithm for designing OCD prediction model, and
making these prediction models available for different biochemical laboratories
for OCD prediction for unlabeled OSBs. Further, from time to time, with
significant growth in the volume of the dataset with labeled samples,
redesigning the prediction model is required for further use. The whole process
requires distributed data collection, data integration, coordination between
the hospital and biochemical laboratory, dynamic machine learning OCD
prediction mode design using a suitable machine learning algorithm, and making
the machine learning model available for the biochemical laboratories. Keeping
all these things in mind, Accu-Help a fully automated, smart, and accurate OCD
detection conceptual model is proposed to help the biochemical laboratories for
efficient detection of OCD from OSBs. OSBs are classified into three classes:
Healthy Individual (HI), OCD Affected Individual (OAI), and Genetically
Affected Individual (GAI). The main component of this proposed framework is the
machine learning OCD prediction model design. In this Accu-Help, a neural
network-based approach is presented with an OCD prediction accuracy of 86
percent.

Machine Learning