Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Department of Pharmacology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.
J Healthc Eng. 2022 Mar 10;2022:7364704. doi: 10.1155/2022/7364704. eCollection 2022.
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.
前列腺癌是全球主要的死亡原因。早期发现和癌症分类对于改善患者健康非常重要。先前的研究利用统计和机器学习 (ML) 技术来检测前列腺癌。然而，研究过程中存在的一些挑战包括高维数据的存在和较少的训练样本数量。元启发式算法可用于解决维度的诅咒，并提高人工智能 (AI) 技术的检测率。基于此动机，本文使用微阵列基因表达数据开发了一种基于人工智能的特征选择与深度学习模型的前列腺癌检测方法 (AIFSDL-PCD)。AIFSDL-PCD 技术包括预处理以提高输入数据的质量。此外，基于混沌入侵杂草优化 (CIWO) 的特征选择 (FS) 技术用于选择最优特征子集，这是工作的新颖之处。此外，深度神经网络 (DNN) 模型可用作分类模型，以检测微阵列基因表达数据中前列腺癌的存在。此外，RMSprop 优化器可有效调整 DNN 模型的超参数。基于 CIWO 的 FS 技术的设计有助于降低计算复杂度并提高分类准确性。实验结果突出了 AIFSDL-PCD 方法在其他技术方面的改进，尤其是在不同的指标上。
