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Cancer Detection and Prediction Using Genetic Algorithms.
Aradhita Bhandari1, B K Tripathy1, Khurram Jawad2
1SITE, VIT, Vellore, Tamil Nadu, India.
Computational Intelligence and Neuroscience
|May 26, 2022
Summary
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.
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.
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