Analysis of Lung Cancer for Developing Smart Healthcare with the Help of BGWO Based TSA-XGBoost Model

Given the circumstances the healthcare system, the Internet of Things (IoT) is crucial. IoT gadgets offer patient data for the framework of healthcare monitoring. IoT is a key component in every aspect of the health care management system since people can use smart devices to check on their health....

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Vydáno v:2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) s. 910 - 916
Hlavní autoři: R, Mahaveerakannan, S, Murali Dhar M, Goswami, Sohan, Krishna, P. Gopala, Jayanthi, K., Kumar, M. Sathish
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 18.10.2023
Témata:
Analytical models
Binary grey wolf optimization algorithm
Classification algorithms
Extreme gradient boosting
Feature extraction
Feature selection
Internet of Things
Lung cancer
Machine learning algorithms
Medical services
Synthetic minority oversampling methods
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Shrnutí:Given the circumstances the healthcare system, the Internet of Things (IoT) is crucial. IoT gadgets offer patient data for the framework of healthcare monitoring. IoT is a key component in every aspect of the health care management system since people can use smart devices to check on their health. Lung cancer is a fatal malignancy, and the likelihood of survival is increased by early identification. Because of the computational difficulty involved in gathering characteristics, it is imperative to design an approach using machine learning techniques for categorising cancer disease because the classification results given by the current methods are inadequate. With the use of the synthetic minority oversampling methods (SMOTE) methodology, the incoming data is pre-processed and balanced. With the help of the binary grey wolf optimisation algorithm (BGWOA), the pertinent features are best chosen. Finally, the suggested model's hyper-parameters are best chosen by the tunicate swarm optimisation (TSA) model, and the classification is carried out by the extreme gradient boosting (XGBoost) model. The experimental analysis demonstrates that the suggested model attained accuracy and recall values of 98% and 95%, respectively, compared to 95% and 95%, respectively, for the identical proposed model without the feature selection (FS) method.
DOI:10.1109/ICSSAS57918.2023.10331761