article · Scientific Reports
Lung cancer poses a major global health challenge, making timely identification and accurate staging essential for effective treatment. To improve classification precision, computational approaches such as binary Greylag Goose Optimisation are paired with a multilayer perceptron neural network. The procedure involves rigorous data preparation, including scaling, normalisation, and handling gap factors, before the algorithm selects the most critical features from complex datasets. Evaluated against several other binary optimisation techniques, binary Greylag Goose Optimisation demonstrated superior capability in refining feature selection. When integrated with the multilayer perceptron model, the hybrid approach achieved a classification accuracy of 98.4 percent. Statistical evaluations using the Wilcoxon signed-rank test and analysis of variance confirmed the effectiveness and adequacy of this hybrid method for lung cancer data classification.
Lung cancer is a leading cause of severe illness and death worldwide, and patient outcomes depend heavily on early and precise detection. By using advanced optimisation techniques to isolate the most relevant data features, computational models can achieve higher diagnostic accuracy, potentially helping clinicians classify cancer cases more reliably from complex clinical data.
The method could support the development of diagnostic decision-support software for clinical pathologists and oncologists assessing lung cancer. Because the findings are based on algorithmic testing and statistical comparisons, the technology remains at an early stage of research. Practical clinical adoption would require extensive integration testing and evaluation on broader real-world patient datasets.
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Lung cancer is an important global health problem, and it is defined by abnormal growth of the cells in the tissues of the lung, mostly leading to significant morbidity and mortality. Its timely identification and correct staging are very important for proper therapy and prognosis. Different computational methods have been used to enhance the precision of lung cancer classification, among which optimization algorithms such as Greylag Goose Optimization (GGO) are employed. These algorithms have the purpose of improving the performance of machine learning models that are presented with a large amount of complex data, selecting the most important features. As per lung cancer classification, data preparation is one of the most important steps, which contains the operations of scaling, normalization, and handling gap factor to ensure reasonable and reliable input data. In this domain, the use of GGO includes refining feature selection, which mainly focuses on enhancing the classification accuracy compared to other binary format optimization algorithms, like bSC, bMVO, bPSO, bWOA, bGWO, and bFOA. The efficiency of the bGGO algorithm in choosing the optimal features for improved classification accuracy is an indicator of the possible application of this method in the field of lung cancer diagnosis. The GGO achieved the highest accuracy with MLP model performance at 98.4%. The feature selection and classification results were assessed using statistical analysis, which utilized the Wilcoxon signed-rank test and ANOVA. The results were also accompanied by a set of graphical illustrations that ensured the adequacy and efficiency of the adopted hybrid method (GGO + MLP).
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DOI: 10.1038/s41598-024-72013-x
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