Abstract
The main intent of this paper is to design and implement a novel methodology for lung cancer prediction using the patient's health record. Feature extraction is performed using two well-performing approaches like principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). As an innovative contribution, a novel optimized correlation-based weighted feature extraction method named as self adaptive sea lion optimization algorithm (SA-SLnO) has been proposed that uses the latest meta-heuristic algorithm to optimize the weights. The number of hidden neurons in RNN is optimized by the proposed SA-SLnO. The major objective of this proposed lung cancer diagnosis model is to minimize the MSE among the actual and predicted outputs. The experimental findings demonstrate that the proposed model outperforms existing methodologies in terms of accuracy, sensitivity, and precision, FPR, FNR, and F1-score. Experimental finding demonstrates that proposed work outperforms PSO-RNN, GWO-RNN, GSO-RNN, and SLnO-RNN in terms of accuracy by 1.7 percent, 2%, 5%, and 2.7%, respectively. Similarly, the proposed lung cancer diagnosis model performed better when all performance metrics are examined on two datasets.
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Pradhan, K., Chawla, P. & Rawat, S. A deep learning-based approach for detection of lung cancer using self adaptive sea lion optimization algorithm (SA-SLnO). J Ambient Intell Human Comput 14, 12933–12947 (2023). https://doi.org/10.1007/s12652-022-04118-y
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DOI: https://doi.org/10.1007/s12652-022-04118-y