Feature optimization and identification of ovarian cancer using internet of medical things

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dc.contributor.authorGhazal, Taher M.
dc.contributor.authorTaleb, Nasser
dc.date.accessioned2022-06-13T16:22:13Z
dc.date.available2022-06-13T16:22:13Z
dc.date.copyright© 2022
dc.date.issued2022-11
dc.description.abstractOvarian cancer (OC) is one kind of tumour that impacts women's ovaries and is hard to diagnose in the initial phase as a primary cause of cancer death. The ovarian cancer information generated by the Clinical Network has been used, and the Self Organizing Map (SOM) and Optimized Neural Networks have suggested a new method for the distinction between ovarian cancer and remaining cancer. Feature optimization and identification of the ovarian cancer (FOI-OV) framework are proposed in this research. The SOM algorithm has also been used separately to improve the functional subset, with understandable and intriguing information from participants' health information steps. The SOM-based collection appears to be tolerable in guided learning strategies due to the lack of different classifiers, which would direct the quest for knowledge specific to the classification algorithm. The classification technique will classify data from ovarian cancer as benign/malignant. By optimizing Neural Network configuration, Advanced Harmony Searching Optimization (AHSO) can enhance the ovarian cancer detection method compared with other methods. This research's suggested model can also diagnose cancer with high precision, and low root means square error (RMSE) early. With 94% precision and 0.029%, RMSE, SOM, and NN techniques have shown identification and precision in ovarian cancer. Optimization (AHSO) has provided an efficient classification approach with a better failure rate. © 2022 John Wiley & Sons Ltd.
dc.identifier.citationGhazal, T. M., & Taleb, N. (2022). Feature optimization and identification of ovarian cancer using internet of medical things. Expert Systems, 39(9), e12987. https://doi.org/10.1111/exsy.12987
dc.identifier.issn02664720
dc.identifier.urihttps://doi.org/10.1111/exsy.12987
dc.identifier.urihttp://hdl.handle.net/20.500.12519/675
dc.language.isoen_US
dc.publisherJohn Wiley and Sons Inc
dc.relationAuthors Affiliations : Ghazal, T.M., Skyline University College, Sharjah, United Arab Emirates, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia; Taleb, N., MIT, Canadian University Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseriesExpert Systems Volume 39, Issue 9
dc.rightsLicense to reuse the abstract has been secured from John Wiley and Sons and Copyright Clearance Center.
dc.rights.holderCopyright: © 2022 John Wiley & Sons Ltd.
dc.subjectfeature classification
dc.subjectinternet of medical things
dc.subjectoptimization
dc.subjectovarian cancer
dc.titleFeature optimization and identification of ovarian cancer using internet of medical things
dc.typeArticle

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