A Fusion-Based Feature Selection Framework for Microarray Data Classification

Almutiri, Talal; Saeed, Faisal; Alassaf, Manar; Hezzam, Essa Abdullah

doi:10.1007/978-3-030-70713-2_52

Cited by 2 publications

(1 citation statement)

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“…Gene expression profiling uses microarray techniques to discover gene patterns when expressed. However, because microarrays produce a large volume of data, the analysis procedure requires a lot of computation power and time [14].…”

Section: A Gene Expressionmentioning

confidence: 99%

Genetic Algorithms and Feature Selection for Improving the Classification Performance in Healthcare

Alassaf,

Alarbeed,

Alrasheed

et al. 2024

IJACSA

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Microarray technology appeared recently and is used in genetic research to study gene expressions. Microarray has been widely applied to many fields, especially the health sector, such as diagnosing and predicting diseases, specifically cancer diseases. These experiments usually generate a huge amount of gene expression data with analytical and computational complexities. Therefore, feature selection techniques and different classifications help solve these problems by eliminating irrelevant and redundant features. This paper presents a proposed method for classifying the data using eight classifications machine learning algorithms. Then, the Genetic Algorithm (GA) is applied to improve the selection of the best features and parameters for the model. We use the higher accuracy of the model among the different classifications as a measure of fit in the genetic algorithm; this means that the model's accuracy can be used to select the best solutions than others in the community. The proposed method was applied to the colon, breast, prostate, and Central Nervous System (CNS) diseases and experimental outcomes demonstrated an accuracy rate of 93.75, 96.15, 82.76, and 93.33 respectively. Based on these findings, the proposed method works well and effectively.

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Section: A Gene Expressionmentioning

confidence: 99%