Data Mining and Mathematical Models in Cancer Prognosis and Prediction

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Published: Sep 1, 2023
DOI: https://doi.org/10.29070/1krjj876
Keywords:
Cancer prognosis, prediction models, machine learning, clinical decision support

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Authors

L Saritha Rani

Research Scholar, Shri Krishna University, Chhatarpur, M.P.

Dr. Dileep Singh

Assistant Professor, Shri Krishna University, Chhatarpur, M.P.

Abstract

There has been tremendous progress in cancer prognosis and prediction thanks to data mining and mathematical modelling techniques. Due to the exponential expansion of genomic, proteomic, and clinical information, conventional diagnostic tools are no longer enough for rapid and reliable evaluations. With the use of mathematical models, we may theoretically estimate tumour development, metastasis, and treatment responses, and data mining can help us find important patterns and information in large datasets relevant to cancer. To improve cancer prognosis and prediction systems, this study investigates how mathematical modelling approaches like differential equations and Markov models, as well as data mining algorithms like neural networks and support vector machines, can work together. In order to help achieve more tailored and accurate cancer treatment, the article goes on to talk about present trends, obstacles, and the potential future of using computational intelligence in oncology.

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Article Details

Issue

Vol. 20 No. 2 (2023): Journal of Advances in Science and Technology (JAST)

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Articles

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