Lung adenocarcinoma, a chronic non-small cell lung cancer, needs to be detected early. Tumor gene expression data analysis is effective for early detection, yet its challenges lie in a small sample size, high dimensionality, and multi-noise characteristics. In this study, we propose a lung adenocarcinoma convolutional neural network (LATCNN), a deep learning model tailored for accurate lung adenocarcinoma prediction and identification of key genes. During the feature selection stage, we introduce a hybrid algorithm. Initially, the fast correlation-based filter (FCBF) algorithm swiftly filters out irrelevant features, followed by applying the k-means-synthetic minority over-sampling technique (k-means-SMOTE) method to address category imbalance. Subsequently, we enhance the particle swarm optimization (PSO) algorithm by incorporating fast-decay dynamic inertia weights and utilizing the classification and regression tree (CART) as the fitness function for the second stage of feature selection, aiming to further eliminate redundant features. In the classifier construction stage, we present an attention convolutional neural network (atCNN) that incorporates an attention mechanism. This improved model conducts feature selection post lung adenocarcinoma gene expression data analysis for classification and prediction. The results show that LATCNN effectively reduces the feature dimensions and accurately identifies 12 key genes with accuracy, recall, F1 score, and MCC of 99.70%, 99.33%, 99.98%, and 98.67%, respectively. These performance metrics surpass those of other comparative models, highlighting the significance of this research for advancing lung adenocarcinoma treatment.
Keywords: LATCNN; attention mechanism; feature selection; k-means-SMOTE; lung adenocarcinoma; tumor gene expression.