Advancement in Machine Learning: A Strategic Lookout from Cancer Identification to Treatment

Bhatt, Maitri; Shende, Pravin

doi:10.1007/s11831-023-09886-0

Cited by 4 publications

(2 citation statements)

References 104 publications

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“…Several studies have reviewed the application of machine learning in cancer research, providing valuable insights into its potential and limitations. 22 – 24 …”

Section: Artificial Intelligence Application To Cancer Researchmentioning

confidence: 99%

The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

Zadeh Shirazi,

Tofighi,

Gharavi

et al. 2024

Technol Cancer Res Treat

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Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, and Deep Learning in oncology, explaining key concepts and algorithms (like SVM, Naïve Bayes, and CNN) in a clear, accessible manner. It aims to make AI advancements understandable to a broad audience, focusing on their application in diagnosing, classifying, and predicting various cancer types, thereby underlining AI's potential to better patient outcomes. Moreover, we present a tabular summary of the most significant advances from the literature, offering a time-saving resource for readers to grasp each study's main contributions. The remarkable benefits of AI-powered algorithms in cancer care underscore their potential for advancing cancer research and clinical practice. This review is a valuable resource for researchers and clinicians interested in the transformative implications of AI in cancer care.

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“…Several studies have reviewed the application of machine learning in cancer research, providing valuable insights into its potential and limitations. 22 – 24 …”

Section: Artificial Intelligence Application To Cancer Researchmentioning

confidence: 99%

The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

Zadeh Shirazi,

Tofighi,

Gharavi

et al. 2024

Technol Cancer Res Treat

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“…Machine learning techniques are increasingly popular in medicine with applications in many different types of illness [ 35 , 36 , 37 ]. There has been some interesting research applying machine learning techniques in the context of inflammatory bowel disease [ 38 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

Gene Identification in Inflammatory Bowel Disease via a Machine Learning Approach

Perez

Castillo

2023

Medicina

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Inflammatory bowel disease (IBD) is an illness with increasing prevalence, particularly in emerging countries, which can have a substantial impact on the quality of life of the patient. The illness is rather heterogeneous with different evolution among patients. A machine learning approach is followed in this paper to identify potential genes that are related to IBD. This is done by following a Monte Carlo simulation approach. In total, 23 different machine learning techniques were tested (in addition to a base level obtained using artificial neural networks). The best model identified 74 genes selected by the algorithm as being potentially involved in IBD. IBD seems to be a polygenic illness, in which environmental factors might play an important role. Following a machine learning approach, it was possible to obtain a classification accuracy of 84.2% differentiating between patients with IBD and control cases in a large cohort of 2490 total cases. The sensitivity and specificity of the model were 82.6% and 84.4%, respectively. It was also possible to distinguish between the two main types of IBD: (1) Crohn’s disease and (2) ulcerative colitis.

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Amalgamation of Artificial Intelligence with Nanoscience for Biomedical Applications

Kasture

Shende

2023

Arch Computat Methods Eng

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Advancement in Machine Learning: A Strategic Lookout from Cancer Identification to Treatment

Cited by 4 publications

References 104 publications

The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

Gene Identification in Inflammatory Bowel Disease via a Machine Learning Approach

Amalgamation of Artificial Intelligence with Nanoscience for Biomedical Applications

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