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. 2022 Mar 10:2022:7364704.
doi: 10.1155/2022/7364704. eCollection 2022.

Optimal Deep Learning Enabled Prostate Cancer Detection Using Microarray Gene Expression

Abdulrhman M Alshareef  1 , Raed Alsini  1 , Mohammed Alsieni  2 , Fadwa Alrowais  3 , Radwa Marzouk  4 , Ibrahim Abunadi  5 , Nadhem Nemri  6
Affiliations

Optimal Deep Learning Enabled Prostate Cancer Detection Using Microarray Gene Expression

Abdulrhman M Alshareef et al. J Healthc Eng. .

Abstract

Prostate cancer is the main cause of death over the globe. Earlier detection and classification of cancer is highly important to improve patient health. Previous studies utilized statistical and machine learning (ML) techniques for prostate cancer detection. However, several challenges that exist in the investigation process are the existence of high dimensionality data and less number of training samples. Metaheuristic algorithms can be used to resolve the curse of dimensionality and improve the detection rate of artificial intelligence (AI) techniques. With this motivation, this article develops an artificial intelligence based feature selection with deep learning model for prostate cancer detection (AIFSDL-PCD) using microarray gene expression data. The AIFSDL-PCD technique involves preprocessing to enhance the input data quality. In addition, a chaotic invasive weed optimization (CIWO) based feature selection (FS) technique for choosing an optimal subset of features shows the novelty of the work. Moreover, the deep neural network (DNN) model can be applied as a classification model to detect the existence of prostate cancer in the microarray gene expression data. Furthermore, the hyperparameters of the DNN model can be effectively adjusted by the use of RMSprop optimizer. The design of CIWO based FS technique helps for reducing the computational complexity and improve the classification accuracy. The experimental results highlighted the betterment of the AIFSDL-PCD approach on the other techniques with respect to distinct measures.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Overall process of AIFSDL-PCD technique.
Figure 2
Figure 2
DNN structure.
Figure 3
Figure 3
Result analysis of optimal DNN technique.
Figure 4
Figure 4
ROC analysis of optimal DNN technique.
Figure 5
Figure 5
Result analysis of AIFSDL-PCD approach.
Figure 6
Figure 6
ROC analysis of AIFSDL-PCD technique.
Figure 7
Figure 7
Accuracy graph analysis of AIFSDL-PCD technique.
Figure 8
Figure 8
Loss graph analysis of AIFSDL-PCD technique.
Figure 9
Figure 9
Accuracy analysis of AIFSDL-PCD technique with existing manners.
Algorithm 1
Algorithm 1
Pseudocode of IWO algorithm.
See this image and copyright information in PMC

References

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