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Cancer Prognosis Using Artificial Intelligence-Based Techniques

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Abstract

Cancer is one of the most dreadful causes of destruction to mankind. Many bioinformatics investigators have applied Artificial Intelligence (AI)-based learning approaches with the aim to develop computationally efficient models for detection of cancerous conditions. Gene expression analysis has shown significant promise in predicting outcomes for several kinds of cancer. Nonetheless, limited sample sizes continue to be a barrier to developing strong and effective classifiers. Traditional supervised learning approaches are limited to labeled data. As a result, a substantial proportion of microarray data sets that lack appropriate follow-up information are ignored. Ability of AI-based deep learning strategies to perceive noteworthy features from intricate datasets exposes their significance. Artificial intelligence and machine learning techniques are making inroads into biological research and health care, including, crucially, cancer research and the healthcare sector, where its practical implications are immense. These include cancer detection and diagnosis, subtype categorization, cancer therapy optimization, and the identification of novel therapeutic pathways in pharmaceutical research. While massive data required to train machine learning models may already exist, capitalizing on this opportunity to realize the fullest potential of artificial intelligence in both cancer research and therapeutic spaces would require significant hurdles to be overcome. The growing requirement is to apply artificial intelligence while maintaining standards to revolutionize cancer diagnosis, prognosis, and treatment of cancer patients and drive biomedical research. In the present study, we put forward a review of the AI-based approaches employed in the most recent publications for cancer prognosis.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Model Institute of Engineering and Technology, Kot Bhalwal, Jammu, Jammu and Kashmir, India

    Surbhi Gupta

  2. Department of Computer Engineering, Indus Institute of Technology & Engineering, Indus University , Ahmedabad, Gujarat, India

    Yogesh Kumar

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  1. Surbhi Gupta
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Correspondence to Surbhi Gupta.

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This article is part of the topical collection “Computational Statistics” guest edited by Anish Gupta, Mike Hinchey, Vincenzo Puri, Zeev Zalevsky and Wan Abdul Rahim.

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Gupta, S., Kumar, Y. Cancer Prognosis Using Artificial Intelligence-Based Techniques. SN COMPUT. SCI. 3, 77 (2022). https://doi.org/10.1007/s42979-021-00964-3

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