Optimal Deep Learning Enabled Prostate Cancer Detection Using Microarray Gene Expression

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.

Prostate Cancer Detection and Analysis using Advanced Machine Learning

“…approach, and a deep neural network (DNN) model. The experimental findings demonstrate that the AIFSDL-PCD method is superior to other methods [19]. Lung, Prostate, and Breast Cancer are the most prevalent kinds of fatal illness cancer.…”

Effective Prostate Cancer Detection using Enhanced Particle Swarm Optimization Algorithm with Random Forest on the Microarray Data

“…The experiments were performed on the public benchmark PC dataset by utilizing a tenfold cross-validation approach to analyze the developed method's performance. Alshareef, [17] developed a Chaotic Invasive Weed Optimization (CIWO) method for selecting the optimum subset of features. Further, the Deep Neural Network (DNN) model was used for detecting the existence of PC.…”

“…The developed model has averagely obtained 95.10% of recall and 95.09% of accuracy on the benchmark PC dataset. Correspondingly, Alshareef,[17] integrated CIWO and DNN for detecting the existence of PC. Hence, the developed CIWO-DNN model achieved 97.25% of recall and 97.19% of accuracy.…”

Advancing prostate cancer detection: a comparative analysis of PCLDA-SVM and PCLDA-KNN classifiers for enhanced diagnostic accuracy

“…They used a technique called scaled variance to normalize the dataset, replacingany values in the dataset with their average value. They used a filter called the flat pattern filter, which eliminates genes to make the dataset that is used for studying biologically meaningful phenomena easier to work with 20 .…”