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

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Abstract

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 \(86\pm 2\%\).

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Data Availability

An earlier version of this paper is made available as a preprint [35].

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Funding

This study was funded by Odisha Higher Education Programme for Excellence and Equity (OHEPEE) World Bank (6770/GMU).

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Authors and Affiliations

  1. Department of Computer Science, Gangadhar Meher University, Sambalpur, India

    Kabita Patel & Ajaya K. Tripathy

  2. Department of CSE, Konark Institute of Science and Technology, Bhubaneswar, India

    Laxmi N. Padhy

  3. Department of Psychiatry, King George’s Medical University, Lucknow, India

    Sujita K. Kar

  4. Department of Psychiatry, All India Institute of Medical Sciences, Bhubaneswar, India

    Susanta K. Padhy

  5. Department of Computer Science and Engineering, University of North Texas, Denton, USA

    Saraju P. Mohanty

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  1. Kabita Patel
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  2. Ajaya K. Tripathy
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Correspondence to Ajaya K. Tripathy.

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Conflict of Interest

The author Ajaya K. Tripathy has received research grants from Odisha Higher Education Programme for Excellence and Equity (OHEPEE) World Bank.

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Patel, K., Tripathy, A.K., Padhy, L.N. et al. Accu-Help: A Machine-Learning-Based Smart Healthcare Framework for Accurate Detection of Obsessive Compulsive Disorder. SN COMPUT. SCI. 5, 36 (2024). https://doi.org/10.1007/s42979-023-02380-1

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