Version 1
: Received: 19 September 2018 / Approved: 19 September 2018 / Online: 19 September 2018 (16:15:56 CEST)
How to cite:
Elhoseny, M.; Bian, G.; Lakshmanaprabu, S.; Shankar, K.; Singh, A.K.; Wu, W. Effective Features to Classify Ovarian Cancer Data in Internet of Medical Things. Preprints2018, 2018090390. https://doi.org/10.20944/preprints201809.0390.v1
Elhoseny, M.; Bian, G.; Lakshmanaprabu, S.; Shankar, K.; Singh, A.K.; Wu, W. Effective Features to Classify Ovarian Cancer Data in Internet of Medical Things. Preprints 2018, 2018090390. https://doi.org/10.20944/preprints201809.0390.v1
Elhoseny, M.; Bian, G.; Lakshmanaprabu, S.; Shankar, K.; Singh, A.K.; Wu, W. Effective Features to Classify Ovarian Cancer Data in Internet of Medical Things. Preprints2018, 2018090390. https://doi.org/10.20944/preprints201809.0390.v1
APA Style
Elhoseny, M., Bian, G., Lakshmanaprabu, S., Shankar, K., Singh, A.K., & Wu, W. (2018). Effective Features to Classify Ovarian Cancer Data in Internet of Medical Things. Preprints. https://doi.org/10.20944/preprints201809.0390.v1
Chicago/Turabian Style
Elhoseny, M., Amit Kumar Singh and Wanqing Wu. 2018 "Effective Features to Classify Ovarian Cancer Data in Internet of Medical Things" Preprints. https://doi.org/10.20944/preprints201809.0390.v1
Abstract
Ovarian Cancer (OC) is a type of cancer that affects ovaries in women, and is difficult to detect at initial stage due to which it remains as one of the leading causes of cancer death. The ovarian cancer data generated from the Internet of Medical Things (IoMT) was used and a novel approach was proposed for distinguishing the ovarian cancer by utilizing Self Organizing Maps (SOM) and Optimal Recurrent Neural Networks (ORNN). 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 supervised learning techniques, the SOM-based feature selection seems to be a tougher challenge because of the absence of class labels that would guide the search for relevant information to the classifier model. The classification approach can identify ovarian cancer data as benign/malignant. The ovarian cancer detection process can be improved by optimizing the weights of RNN structure using Adaptive Harmony Search Optimization (AHSO). The proposed model in this study can be used to detect cancer at early stages with high accuracy and low Root Mean Square Error (RMSE).
Keywords
Ovarian Cancer; Features Classification; Self-Organizing Map; Optimal Neural Networks; Adaptive Harmony Search Optimization; Internet of Things
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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