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• W2890597972 abstract "Ovarian Cancer (OC) is a type of cancer that affects ovaries in women, and is difficult to detect at initial stage resulting to increased mortality rate. The OC data generated from the Internet of Medical Things (IoMT) can be used to identify distinguish the OC. To achieve this, we utilize Self Organizing Maps (SOM) and Optimal Recurrent Neural Networks (ORNN) to classify OC. SOM algorithm was utilized for better feature subset selection and was also utilized for separating profitable, understood and intriguing data from huge measures of medical data. In addition, an optimal classifier named optimal recurrent neural network (ORNN) is also employed. The classification rate of OC detection process can be improved by optimizing the weights of RNN structure using Adaptive Harmony Search Optimization (AHSO) algorithm. A set of experimentation is carried out using the data collected from women who have a high danger of OC because of familial or individual history of cancer. The proposed method attains a maximum accuracy of 96.27 with the sensitivity and specificity rate of 85.2 respectively when compared to recurrent neural networks (RNN), feedforward neural networks (FFNN) and so on. The experimental results verified that the proposed model can be used to detect cancer at early stages with high accuracy, sensitivity, specificity and low root mean square error (RMSE)." @default.
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• W2890597972 date "2019-08-01" @default.
• W2890597972 modified "2023-10-12" @default.
• W2890597972 title "Effective features to classify ovarian cancer data in internet of medical things" @default.
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• W2890597972 doi "https://doi.org/10.1016/j.comnet.2019.04.016" @default.
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