An Innovative Artificial Intelligence Based Decision Making System for Public Health Crisis Virtual Reality Rehabilitation
Hayder M A Ghanimi
Department of Information Technology, College of Science, University of Warith Al-Anbiyaa, Karbala, Iraq, Department of Computer Science, College of Computer Science and Information Technology, University of Kerbala, Karbala, Iraq.
Department of Computer Science and Artificial Intelligence, School of Computer Science and Artificial Intelligence, SR University, Warangal - 506371, Telangana, India.
The COVID-19 disease caused by the SARS-CoV-2 virus was declared by the World Health Organization (WHO) as a spreadable viral disease. During the COVID pandemic, there was difficulty in notifying the Decision-Making System (DMS) about the rapid and precise triage of patients admitted to the emergency wards. As a method to achieve the aim and develop digital healthcare revolutions in data and analytics, digital healthcare information was established. Artificial Intelligence (AI) is a robust automation tool for sustainability in the context of the COVID-19 health crisis on big datasets. Besides, the gap between AI investment and commercial real-time application, which are the initial digital technology development curves, has been identified. It was discovered that AI’s new applications are grounded in Digital Transformation Mapping (DTM) for the DMS of Health Crises. The fast inventions in AI and Machine Learning (ML) have implications for amazingly preventive and clinical healthcare, and for the association, ML was developed as a predictable attention. Billions of smartphones, massive online datasets, linked wireless wearable devices, comparatively cost-effective computing resources and improved ML and Nural Language Processing (NLP) are leveraged by these rapid responses, with the trained dataset of 65% and evaluated in the other 35%, the renowned ML models for structured data like Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), Logistic Regressive Tree (LRT), Decision Tree (DT), Stochastic Gradient Booster (SGB), and Random Forest (RF) are used for simulating new unidentified data. AI-DTM challenges DMS of Health Crises (COVID-19) and the drawbacks of critically contributing risk factors to healthcare diseases. Meanwhile, a comprehensive collection of healthcare datasets over what is spreadable would be required to save human lives, train AI, and limit cost-effective health risks.
Keywords
Artificial Intelligence, Data Science, Machine Learning, Digital Transformation Mapping, Health Crises, Decision Making Systems.
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The authors confirm contribution to the paper as follows:
Conceptualization: Hayder M A Ghanimi, Firas Tayseer Ayasrah, Vijaya Chandra Jadala, Manjunath T C, Balasaranya K and Srinivasarao B;
Methodology: Manjunath T C, Balasaranya K and Srinivasarao B;
Software: Hayder M A Ghanimi, Firas Tayseer Ayasrah and Vijaya Chandra Jadala;
Data Curation: Manjunath T C, Balasaranya K and Srinivasarao B;
Writing- Original Draft Preparation: Hayder M A Ghanimi, Firas Tayseer Ayasrah and Vijaya Chandra Jadala;
Visualization: Hayder M A Ghanimi, Firas Tayseer Ayasrah and Vijaya Chandra Jadala;
Investigation: Firas Tayseer Ayasrah, Vijaya Chandra Jadala and Manjunath T C;
Supervision: Hayder M A Ghanimi, Firas Tayseer Ayasrah and Vijaya Chandra Jadala;
Validation: Manjunath T C, Balasaranya K and Srinivasarao B;
All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
The authors would like to thank to the reviewers for nice comments on the manuscript.
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Corresponding author
Hayder M A Ghanimi
Department of Information Technology, College of Science, University of Warith Al-Anbiyaa, Karbala, Iraq, Department of Computer Science, College of Computer Science and Information Technology, University of Kerbala, Karbala, Iraq.
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Cite this article
Hayder M A Ghanimi, Firas Tayseer Ayasrah, Vijaya Chandra Jadala, Manjunath T C, Balasaranya K and Srinivasarao B, “An Innovative Artificial Intelligence Based Decision Making System for Public Health Crisis Virtual Reality Rehabilitation”, Journal of Machine and Computing, vol.5, no.1, pp. 561-575, January 2025, doi: 10.53759/7669/jmc202505044.