A Non Invasive Hybrid Machine Learning Technique for Prediction of Multiple Psychological Diseases

Masoud Mohammadian; Vikas Kamra; Praveen Kumar; Brahmdeep  Sindhu; Ravinder Puri

doi:10.1109/Confluence56041.2023.10048877

A Non Invasive Hybrid Machine Learning Technique for Prediction of Multiple Psychological Diseases

Masoud Mohammadian, Vikas Kamra, Praveen Kumar, Brahmdeep Sindhu, Ravinder Puri

Information, Technology (IT) & Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

Abstract

Psychological Stress is a crucial factor in any person's health and is closely related to many illnesses, including anxiety, depression and other mental disorders. Therefore, it is crucial to keep an eye on individuals' improper behavior and provide the appropriate treatment plan. Al enabled artificial intelligence based decision taking systems and disease diagnostics have attracted a lot of interest from experts and the global research communities. There are many algorithms created and used by using data mining and machine learning based techniques that may significantly participate in the improvement of the health infrastructure and can support professionals engaged in the early detection of illnesses. Proposed system offer an additional layer that benefits patients and the healthcare services professionals through time saving during early diagnosis of critical diseases. The novel research findings presented here will aid in the identification of several mental illnesses, such as schizophrenia, obsessive-compulsive disorder, and bipolar disorder. The primary goal of the suggested approach is to develop a model which will help individuals as well as medical professionals to early detect any psychological disease and helps to start its treatment.

Original language	English
Title of host publication	Proceedings of the 13th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2023
Editors	Balvinder Shukla
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	6-11
Number of pages	6
Volume	1
Edition	1
ISBN (Electronic)	9781665462631
ISBN (Print)	9781665462648
DOIs	https://doi.org/10.1109/Confluence56041.2023.10048877
Publication status	Published - Jan 2023
Event	2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence) - India, Delhi, India Duration: 19 Jan 2023 → 20 Jan 2023 https://www.amity.edu/aset/confluence2023/

Publication series

Name	Proceedings of the 13th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2023

Conference

Conference	2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence)
Country/Territory	India
City	Delhi
Period	19/01/23 → 20/01/23
Internet address	https://www.amity.edu/aset/confluence2023/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/Confluence56041.2023.10048877

Cite this

Mohammadian, M., Kamra, V., Kumar, P., Sindhu, B., & Puri, R. (2023). A Non Invasive Hybrid Machine Learning Technique for Prediction of Multiple Psychological Diseases. In B. Shukla (Ed.), Proceedings of the 13th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2023 (1 ed., Vol. 1, pp. 6-11). (Proceedings of the 13th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2023). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/Confluence56041.2023.10048877

Mohammadian, Masoud ; Kamra, Vikas ; Kumar, Praveen et al. / A Non Invasive Hybrid Machine Learning Technique for Prediction of Multiple Psychological Diseases. Proceedings of the 13th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2023. editor / Balvinder Shukla. Vol. 1 1. ed. United States : IEEE, Institute of Electrical and Electronics Engineers, 2023. pp. 6-11 (Proceedings of the 13th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2023).

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title = "A Non Invasive Hybrid Machine Learning Technique for Prediction of Multiple Psychological Diseases",

abstract = "Psychological Stress is a crucial factor in any person's health and is closely related to many illnesses, including anxiety, depression and other mental disorders. Therefore, it is crucial to keep an eye on individuals' improper behavior and provide the appropriate treatment plan. Al enabled artificial intelligence based decision taking systems and disease diagnostics have attracted a lot of interest from experts and the global research communities. There are many algorithms created and used by using data mining and machine learning based techniques that may significantly participate in the improvement of the health infrastructure and can support professionals engaged in the early detection of illnesses. Proposed system offer an additional layer that benefits patients and the healthcare services professionals through time saving during early diagnosis of critical diseases. The novel research findings presented here will aid in the identification of several mental illnesses, such as schizophrenia, obsessive-compulsive disorder, and bipolar disorder. The primary goal of the suggested approach is to develop a model which will help individuals as well as medical professionals to early detect any psychological disease and helps to start its treatment.",

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author = "Masoud Mohammadian and Vikas Kamra and Praveen Kumar and Brahmdeep Sindhu and Ravinder Puri",

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Mohammadian, M, Kamra, V, Kumar, P, Sindhu, B & Puri, R 2023, A Non Invasive Hybrid Machine Learning Technique for Prediction of Multiple Psychological Diseases. in B Shukla (ed.), Proceedings of the 13th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2023. 1 edn, vol. 1, Proceedings of the 13th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2023, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 6-11, 2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Delhi, India, 19/01/23. https://doi.org/10.1109/Confluence56041.2023.10048877

A Non Invasive Hybrid Machine Learning Technique for Prediction of Multiple Psychological Diseases. / Mohammadian, Masoud; Kamra, Vikas; Kumar, Praveen et al.
Proceedings of the 13th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2023. ed. / Balvinder Shukla. Vol. 1 1. ed. United States: IEEE, Institute of Electrical and Electronics Engineers, 2023. p. 6-11 (Proceedings of the 13th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2023).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - A Non Invasive Hybrid Machine Learning Technique for Prediction of Multiple Psychological Diseases

AU - Mohammadian, Masoud

AU - Kamra, Vikas

AU - Kumar, Praveen

AU - Sindhu, Brahmdeep

AU - Puri, Ravinder

PY - 2023/1

Y1 - 2023/1

N2 - Psychological Stress is a crucial factor in any person's health and is closely related to many illnesses, including anxiety, depression and other mental disorders. Therefore, it is crucial to keep an eye on individuals' improper behavior and provide the appropriate treatment plan. Al enabled artificial intelligence based decision taking systems and disease diagnostics have attracted a lot of interest from experts and the global research communities. There are many algorithms created and used by using data mining and machine learning based techniques that may significantly participate in the improvement of the health infrastructure and can support professionals engaged in the early detection of illnesses. Proposed system offer an additional layer that benefits patients and the healthcare services professionals through time saving during early diagnosis of critical diseases. The novel research findings presented here will aid in the identification of several mental illnesses, such as schizophrenia, obsessive-compulsive disorder, and bipolar disorder. The primary goal of the suggested approach is to develop a model which will help individuals as well as medical professionals to early detect any psychological disease and helps to start its treatment.

AB - Psychological Stress is a crucial factor in any person's health and is closely related to many illnesses, including anxiety, depression and other mental disorders. Therefore, it is crucial to keep an eye on individuals' improper behavior and provide the appropriate treatment plan. Al enabled artificial intelligence based decision taking systems and disease diagnostics have attracted a lot of interest from experts and the global research communities. There are many algorithms created and used by using data mining and machine learning based techniques that may significantly participate in the improvement of the health infrastructure and can support professionals engaged in the early detection of illnesses. Proposed system offer an additional layer that benefits patients and the healthcare services professionals through time saving during early diagnosis of critical diseases. The novel research findings presented here will aid in the identification of several mental illnesses, such as schizophrenia, obsessive-compulsive disorder, and bipolar disorder. The primary goal of the suggested approach is to develop a model which will help individuals as well as medical professionals to early detect any psychological disease and helps to start its treatment.

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KW - Obsessive-Compulsive Disorder

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KW - Schizophrenia

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M3 - Conference contribution

SN - 9781665462648

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BT - Proceedings of the 13th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2023

A2 - Shukla, Balvinder

PB - IEEE, Institute of Electrical and Electronics Engineers

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Mohammadian M, Kamra V, Kumar P, Sindhu B, Puri R. A Non Invasive Hybrid Machine Learning Technique for Prediction of Multiple Psychological Diseases. In Shukla B, editor, Proceedings of the 13th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2023. 1 ed. Vol. 1. United States: IEEE, Institute of Electrical and Electronics Engineers. 2023. p. 6-11. (Proceedings of the 13th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2023). doi: 10.1109/Confluence56041.2023.10048877

A Non Invasive Hybrid Machine Learning Technique for Prediction of Multiple Psychological Diseases

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