Cancer Detection and Prediction Using Genetic Algorithms

Overview

Journal Comput Intell Neurosci

Specialty Biology

Date 2022 May 26

PMID 35615545

Authors

Aradhita Bhandari

B K Tripathy

Khurram Jawad

Surbhi Bhatia

Mohammad Khalid Imam Rahmani

Arwa Mashat

Affiliations

Soon will be listed here.

Abstract

Cancer is a wide category of diseases that is caused by the abnormal, uncontrollable growth of cells, and it is the second leading cause of death globally. Screening, early diagnosis, and prediction of recurrence give patients the best possible chance for successful treatment. However, these tests can be expensive and invasive and the results have to be interpreted by experts. Genetic algorithms (GAs) are metaheuristics that belong to the class of evolutionary algorithms. GAs can find the optimal or near-optimal solutions in huge, difficult search spaces and are widely used for search and optimization. This makes them ideal for detecting cancer by creating models to interpret the results of tests, especially noninvasive. In this article, we have comprehensively reviewed the existing literature, analyzed them critically, provided a comparative analysis of the state-of-the-art techniques, and identified the future challenges in the development of such techniques by medical professionals.

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