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

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John Wiley and Sons Inc
Ovarian 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.
This article is not available at the CUD collection. The version of the scholarly record of this paper is published in Expert Systems (2022), available online at: https://doi.org/10.1111/exsy.12987
feature classification, internet of medical things, optimization, ovarian cancer
Ghazal, T. M., & Taleb, N. (2022). Feature optimization and identification of ovarian cancer using internet of medical things. Expert Systems. https://doi.org/10.1111/exsy.12987