A Comprehensive Review Of Machine Learning, Deep Learning, Hybrid, And Explainable AI Methods For Cancer Prediction: A Systematic Study

Devi Gopal.T; Dr.J A M Rexie

doi:10.29284/0b0ff395

Authors

Devi Gopal.T Research Scholar, CSE Karunya Insititute of Technology and Sciences Coiambathoor
Dr.J A M Rexie AP, CSE Karunya Insititute of Technology and Sciences Coiambathoor

DOI:

https://doi.org/10.29284/0b0ff395

Keywords:

Cancer prediction, Machine learning, Deep learning, Hybrid models, Explainable AI, Medical imaging, Multi-omics.

Abstract

Cancer remains one of the leading causes of mortality worldwide, necessitating early detection and accurate prediction systems. Artificial Intelligence (AI), particularly Machine Learning (ML), Deep Learning (DL), Hybrid learning frameworks, and Explainable AI (XAI), has significantly advanced cancer diagnosis and prognosis. This paper presents a systematic review of over 60 recently published studies (2020–2025) focusing on AI-driven cancer prediction across imaging, genomic, clinical, and multimodal datasets. We categorize the reviewed works into four primary groups: (i) Machine Learning-based methods, (ii) Deep Learning-based approaches, (iii) Hybrid ML–DL frameworks, and (iv) Explainable AI-integrated models. We analyze datasets used, performance metrics achieved, architectural trends, and methodological innovations. Furthermore, we identify key research gaps, including limited clinical validation, lack of standardized XAI evaluation metrics, and insufficient uncertainty quantification. This review provides a structured foundation for future research in trustworthy, multimodal cancer prediction systems.

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