Nature-Inspired Algorithms of Machine Learning and Generative AI for Cancer Diagnosis and Treatment

Abstract

Nature-inspired algorithms have proved to give accurate results in detecting and providing treatment for various types of cancers. These optimized algorithms exhibit efficiency with a focus on finding the best solutions to highly complex problems. With the strategies of selection, reproduction, crossover, and mutation, and a set of novel problem-solving methodologies derived from natural processes (based on Darwin’s theory of survival of the fittest), working on a varied population of data items and supported by computations in generations or layers, these algorithms achieve high accuracy. The present chapter concentrates on presenting work done for the diagnosis of various types of cancer and, further, when a specific type of cancer is detected, the precise treatment that can be applied to control its growth. Various nature-inspired algorithms from machine learning and generative AI are used to give the required results. The results achieved are promising and comparable to those achieved in standard literature.

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