Lung Cancer Prediction Using Improved SALP Swarm Optimization and LSTM Human Gene Classification.
Keywords:
Lung cancer prediction, Human Gene Selection, Quantum Ant Lion, Improved SalpSwarm Algorithm, Hyper parameter initializationAbstract
The medical domain requires a gene selection model to manage cancer effectively. The large amount of gene information makes it difficult for the existing model to analyze the relationships between the features. In addition to local optima traps, lower convergence, and overfitting, the existing models have other limitations. The Quantum Ant Lion (QAL) is proposed to select features from gene datasets along with the Improved Salp Swarm Algorithm (ISSA) to initiate the classification. Salp swarm optimization is used in the salp swarm method to increase search efficiency, thereby increasing exploration and overcoming the local optima trap. To increase exploitation in feature selection, the ISSA hyperparameter is applied. The proposed ISSA-LSTM technique increases exploration and exploitation, thus increasing the accuracy rate of the lung cancer classification. LSTM are used to classify the emotional features of the lung cancer. The performance of the ISSA-LSTM method in analyzed in the terms of accuracy, specificity, recall, F-measure and MCC. The ISSA-LSTM method has 99.54 % accuracy for gene classification using micro array gene dataset.
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