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Cancer Detection and Prediction Using Genetic Algorithms.

Aradhita Bhandari1, B K Tripathy1, Khurram Jawad2

  • 1SITE, VIT, Vellore, Tamil Nadu, India.

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|May 26, 2022
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Summary
This summary is machine-generated.

Genetic algorithms (GAs) offer a powerful approach for cancer detection by creating models to interpret complex test results, especially for noninvasive methods. This review explores GAs for optimizing cancer diagnosis and recurrence prediction.

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Area of Science:

  • Oncology
  • Computational Biology
  • Bioinformatics

Background:

  • Cancer is a leading global cause of death, necessitating improved screening and diagnostic methods.
  • Current cancer detection methods can be costly, invasive, and require expert interpretation.
  • Genetic algorithms (GAs) are optimization techniques with potential for improving cancer diagnostics.

Purpose of the Study:

  • To review and critically analyze the application of genetic algorithms in cancer detection.
  • To provide a comparative analysis of state-of-the-art GA techniques for cancer diagnosis.
  • To identify future challenges and opportunities in using GAs for medical professionals.

Main Methods:

  • Comprehensive literature review on genetic algorithms in cancer research.
  • Critical analysis of existing studies and techniques.
  • Comparative analysis of different GA-based models for cancer detection.

Main Results:

  • Genetic algorithms demonstrate significant potential for creating models to interpret complex diagnostic data.
  • GAs are well-suited for optimizing the analysis of noninvasive cancer detection methods.
  • The review highlights the effectiveness of GAs in search and optimization for cancer-related data.

Conclusions:

  • Genetic algorithms represent a promising tool for enhancing cancer screening, early diagnosis, and recurrence prediction.
  • Further development and integration of GAs can lead to more accessible and accurate cancer diagnostics.
  • Addressing future challenges will be crucial for the successful clinical implementation of GA-based techniques.