A Survey on Evolutionary Algorithms Based Machine Learning Approaches for Medical Diagnosis

The use of extended time series to gather and store huge datasets has been made simpler by information science and data capture technologies. In many fields, such as astronomy, the environment, economics, business, medical, data analysis, and knowledge mining, these datasets are growing in popularit...

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Bibliographic Details
Published in2024 International Conference on Modeling, Simulation & Intelligent Computing (MoSICom) pp. 234 - 239
Main Authors Hossen, Md. Jakir, Ramanathan, Thirumalaimuthu Thirumalaiappan, Raja, Joseph Emerson
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.12.2024
Subjects
Data mining
Data preprocessing
Evolutionary computation
Genetic algorithms
Machine learning
medical diagnosis
Medical diagnostic imaging
Reviews
Surveys
Time series analysis
Tuning
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DOI10.1109/MoSICom63082.2024.10880934

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Summary:The use of extended time series to gather and store huge datasets has been made simpler by information science and data capture technologies. In many fields, such as astronomy, the environment, economics, business, medical, data analysis, and knowledge mining, these datasets are growing in popularity. Finding hidden patterns, connections, and anomalies in massive databases using statistical analysis and machine learning is known as data mining. You can use this information to help with comprehending complex phenomena, generating predictions, and making decision. The evolutionary computational techniques and machine learning techniques play a main role in the data mining process. This paper reviews various machine learning approaches that are based on different evolutionary algorithms such as genetic algorithm, particle swarm optimization, and ant colony optimization for the classification of medical datasets. The comparison and discussion are made between different evolutionary algorithms-based techniques for the medical data classification process. The review of evolutionary algorithms-based classification approaches showed that the performance of evolutionary algorithms varied according to the medical datasets. The reviewed classification approaches also lead to the conclusion that evolutionary algorithms are essential for the data preprocessing and parameters tuning in machine learning based classifiers.
DOI:10.1109/MoSICom63082.2024.10880934