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Performance Analysis of Machine Learning and Soft Computing Techniques in Diagnosis of Behavioral Disorders

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Electronic Systems and Intelligent Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 860))

Abstract

Behavioral disorders are primary psychological human disorders that represent a disturbance in an individual's behavior, emotional and cognitive system. These human disorders can be well diagnosed using emerging machine learning and soft computing techniques. Here, different behavioral disorders, their symptoms, and associated consequences have been identified and summarized. The key intention of this work is to highlight the applications of machine learning and soft computing techniques used in the diagnosis of these human psychiatric conditions. The use of these methodologies in the diagnosis of behavioral disorders is limited, compared to other human diseases (diabetes, cardio, cancer). Several machine learning and soft computing techniques, viz., SVM, ELM, KNN, CNN, and fuzzy inference have been used to diagnose different behavioral disorders. As per the literature, the highest accuracy rate achieved in the diagnosis of attention deficit hyperactivity disorder, conduct disorder, tic disorder, and anxiety is 98.62%, 85%, 90.1%, and 97%, respectively. However, there is still a promising opportunity to use these techniques to examine the symptoms and history to diagnose the problem, develop tools to assist psychiatrists in predicting psychological disorders, and support patient care.

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Author information

Authors and Affiliations

  1. Department of CSA, DAV University, Jalandhar, India

    Preeti Monga, Manik Sharma & Sanjeev Kumar Sharma

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  1. Preeti Monga
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  2. Manik Sharma
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  3. Sanjeev Kumar Sharma
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Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology(KIIT) Deemed to be University, Bhubaneswar, Odisha, India

    Pradeep Kumar Mallick

  2. KIET Group of Institutions, Delhi-NCR, Ghaziabad, India

    Akash Kumar Bhoi

  3. Research on Agent based, Social and Interdisiciplinary Applications (GRASIA), Complutense University of Madrid, Madrid, Spain

    Alfonso González-Briones

  4. School of Computer Engineering, Kalinga Institute of Industrial Technology(KIIT) Deemed to be University, Bhubaneswar, Odisha, India

    Prasant Kumar Pattnaik

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Monga, P., Sharma, M., Sharma, S.K. (2022). Performance Analysis of Machine Learning and Soft Computing Techniques in Diagnosis of Behavioral Disorders. In: Mallick, P.K., Bhoi, A.K., González-Briones, A., Pattnaik, P.K. (eds) Electronic Systems and Intelligent Computing. Lecture Notes in Electrical Engineering, vol 860. Springer, Singapore. https://doi.org/10.1007/978-981-16-9488-2_8

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  • DOI: https://doi.org/10.1007/978-981-16-9488-2_8

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