Effective features to classify ovarian cancer data in internet of medical things

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).

[1]  Leslie Z Benet,et al.  Classification of natural products as sources of drugs according to the biopharmaceutics drug disposition classification system (BDDCS). , 2016, Chinese journal of natural medicines.

[2]  Victor Hugo C. de Albuquerque,et al.  A proposal for Internet of Smart Home Things based on BCI system to aid patients with amyotrophic lateral sclerosis , 2018, Neural Computing and Applications.

[3]  Guanglin Li,et al.  Optimization of signal quality over comfortability of textile electrodes for ECG monitoring in fog computing based medical applications , 2018, Future Gener. Comput. Syst..

[4]  M. Fesanghary,et al.  An improved harmony search algorithm for solving optimization problems , 2007, Appl. Math. Comput..

[5]  D. Badgwell,et al.  Early Detection of Ovarian Cancer , 2007, Disease markers.

[6]  Xiang Ji,et al.  Representing and Retrieving Video Shots in Human-Centric Brain Imaging Space , 2013, IEEE Transactions on Image Processing.

[7]  Raimir Holanda Filho,et al.  Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering , 2019, IEEE Network.

[8]  David J. Brown,et al.  A new hybrid global optimization approach for selecting clinical and biological features that are relevant to the effective diagnosis of ovarian cancer , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[9]  Shou-Hsuan Stephen Huang,et al.  User Behavior Analysis in Masquerade Detection Using Principal Component Analysis , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[10]  Valery V. Korotaev,et al.  A Reference Model for Internet of Things Middleware , 2018, IEEE Internet of Things Journal.

[11]  Yuan-Ting Zhang,et al.  Heartbeats Based Biometric Random Binary Sequences Generation to Secure Wireless Body Sensor Networks , 2018, IEEE Transactions on Biomedical Engineering.

[12]  M.Y. Mashor,et al.  Intelligent classification system for cancer data based on artificial neural network , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[13]  Fizazi Hadria,et al.  Unsupervised Clustering of Images Using Harmony Search Algorithm , 2013 .

[14]  Kulvinder Singh Mann,et al.  Ovarian cancer stage based detection on convolutional neural network , 2017, 2017 2nd International Conference on Communication and Electronics Systems (ICCES).

[15]  Emanuel F. Petricoin,et al.  High-resolution serum proteomic features for ovarian cancer detection. , 2004 .

[16]  Dian Pratiwi,et al.  The Use of Self Organizing Map Method and Feature Selection in Image Database Classification System , 2012, ArXiv.

[17]  Joel J. P. C. Rodrigues,et al.  Effective Features to Classify Big Data Using Social Internet of Things , 2018, IEEE Access.

[18]  H. Hollema,et al.  Characteristics of Lynch syndrome associated ovarian cancer. , 2018, Gynecologic oncology.

[19]  Mário Antunes,et al.  Towards IoT data classification through semantic features , 2017, Future Gener. Comput. Syst..

[20]  A. Vlahou,et al.  Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data , 2003, Journal of biomedicine & biotechnology.

[21]  Narayan Ganesan,et al.  Application of Neural Networks in Diagnosing Cancer Disease using Demographic Data , 2010 .

[22]  Joel J. P. C. Rodrigues,et al.  Enabling Technologies for the Internet of Health Things , 2018, IEEE Access.

[23]  King Ngi Ngan,et al.  Unsupervised extraction of visual attention objects in color images , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[24]  Naveen K. Chilamkurti,et al.  In-Mapper combiner based MapReduce algorithm for processing of big climate data , 2018, Future Gener. Comput. Syst..

[25]  Aditya Singh,et al.  Novel ABC based training algorithm for ovarian cancer detection using neural network , 2017, 2017 International Conference on Trends in Electronics and Informatics (ICEI).

[26]  P Yasodha,et al.  Detecting the ovarian cancer using big data analysis with effective model , 2018 .

[27]  Joel J. P. C. Rodrigues,et al.  Enabling Technologies on Cloud of Things for Smart Healthcare , 2018, IEEE Access.

[28]  Kym Faull,et al.  Characterization of serum biomarkers for detection of early stage ovarian cancer , 2005, Proteomics.

[29]  Andino Maseleno,et al.  Optimal feature-based multi-kernel SVM approach for thyroid disease classification , 2018, The Journal of Supercomputing.

[30]  Mourad Zaied,et al.  Unsupervised Features Extraction Using a Multi-view Self Organizing Map for Image Classification , 2017, 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA).

[31]  Kartik Shankar,et al.  Random forest for big data classification in the internet of things using optimal features , 2019, Int. J. Mach. Learn. Cybern..

[32]  Benjamin Schrauwen,et al.  Training and Analysing Deep Recurrent Neural Networks , 2013, NIPS.

[33]  Xuanjing Huang,et al.  Recurrent Neural Network for Text Classification with Multi-Task Learning , 2016, IJCAI.

[34]  Heye Zhang,et al.  Assessment of Biofeedback Training for Emotion Management Through Wearable Textile Physiological Monitoring System , 2015, IEEE Sensors Journal.

[35]  P. Yasodha,et al.  Analysing Big Data to Build Knowledge Based System for Early Detection of Ovarian Cancer , 2015 .

[36]  Hiok Chai Quek,et al.  Ovarian Cancer Diagnosis with Complementary Learning Fuzzy Neural Network , 2022 .

[37]  N. Arunkumar,et al.  Optimal deep learning model for classification of lung cancer on CT images , 2019, Future Gener. Comput. Syst..

[38]  B J McNeil,et al.  Staging of advanced ovarian cancer: comparison of imaging modalities--report from the Radiological Diagnostic Oncology Group. , 2000, Radiology.

[39]  Feng Wu,et al.  Background Prior-Based Salient Object Detection via Deep Reconstruction Residual , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[40]  Kunle Odunsi,et al.  Hereditary association between testicular cancer and familial ovarian cancer: A Familial Ovarian Cancer Registry study. , 2018, Cancer Epidemiology.