Accu-Help: A Machine-Learning-Based Smart Healthcare Framework for Accurate Detection of Obsessive Compulsive Disorder

• Published in: SN computer science Vol. 5; no. 1; p. 36
• Main Authors: Patel, Kabita, Tripathy, Ajaya K., Padhy, Laxmi N., Kar, Sujita K., Padhy, Susanta K., Mohanty, Saraju P.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.01.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Automation; Biochemistry; Biomarkers; Classification; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data collection; Data integration; Data Structures and Information Theory; Datasets; Health care; Hormones; Hospitals; Information Systems and Communication Service; Laboratories; Machine learning; Neural networks; Obsessive compulsive disorder; Original Research; Oxidative stress; Patients; Pattern Recognition and Graphics; Prediction models; Sensors; Software Engineering/Programming and Operating Systems; Support vector machines; Vision; Smart healthcare, Internet-of-Medical-Things (IoMT); Healthcare Cyber-Physical System (H-CPS); Artificial neural network (ANN); Obsessive compulsive disorder (OCD); Machine learning
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-023-02380-1

Smart healthcare becomes one of the popular research areas in recent years. This research proposes to expand the state-of-art of smart healthcare by incorporating solutions for obsessive compulsive disorder (OCD). Classification of OCD by analyzing oxidative stress biomarkers (OSBs) through a machine-learning mechanism is a significant development in the study of OCD. However, this procedure requires 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 entire process demands distributed data collection, data integration, coordination between the hospital and the biochemical laboratory in real-time, dynamic machine-learning model design for OCD prediction, and making the machine-learning model available for the biochemical laboratories. Considering these requirements, 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-based OCD class prediction model design. Accu-Help uses a neural network-based approach with an OCD class prediction accuracy of 86 ± 2 % .