An intelligent method of cancer prediction based on mobile cloud computing

In recent years, mobile medical services have gradually become the common focus of the communications and healthcare industries. People can get medical information and services efficiently and conveniently anytime, anywhere. However, due to the limitations of the mobile terminal’s own computing and storage capabilities, mobile therapy is greatly challenged. Therefore, we propose a mobile medical health system based on cloud computing. Firstly, principal component analysis was used to obtain representative features. Then, a simplified feature subset was applied to support vector machine (SVM) based on Sigmoid kernel function. The data set was categorized by SVM as cancer patient and normal object experimental results show that the method is improved in accuracy, sensitivity, and specificity.

[1]  Chonho Lee,et al.  A survey of mobile cloud computing: architecture, applications, and approaches , 2013, Wirel. Commun. Mob. Comput..

[2]  Ilias Maglogiannis,et al.  Wavelet-Based Compression With ROI Coding Support for Mobile Access to DICOM Images Over Heterogeneous Radio Networks , 2009, IEEE Transactions on Information Technology in Biomedicine.

[3]  A. Jemal,et al.  Cancer statistics, 2016 , 2016, CA: a cancer journal for clinicians.

[4]  Sunita Soni,et al.  Naive Bayes Classifiers: A Probabilistic Detection Model for Breast Cancer , 2014 .

[5]  A. Jemal,et al.  Cancer treatment and survivorship statistics, 2012 , 2012, CA: a cancer journal for clinicians.

[6]  A. Jemal,et al.  Colorectal cancer statistics, 2017 , 2017, CA: a cancer journal for clinicians.

[7]  D. Dutta Majumder,et al.  An Investigative Study on Early Diagnosis of Prostate Cancer Using Neuro-Fuzzy Classification System for Pattern Recognition , 2017, Int. J. Fuzzy Syst..

[8]  P. Hamsagayathri,et al.  DECISION TREE CLASSIFIERS FOR CLASSIFICATION OF BREAST CANCER , 2017 .

[9]  A. Jemal,et al.  Colorectal cancer statistics, 2014 , 2014, CA: a cancer journal for clinicians.

[10]  Alaa Eldin M. Riad,et al.  A Framework for Ovarian Cancer Diagnosis Based on Amino Acids Using Fuzzy-Rough Sets with SVM , 2014, AMLTA.

[11]  I. Jolliffe.  Principal Component Analysis and Factor Analysis , 1986 .

[12]  Wei Li,et al.  Prostate cancer diagnosis using deep learning with 3D multiparametric MRI , 2017, Medical Imaging.

[13]  Beatriz A. Garro,et al.  Classification of DNA microarrays using artificial neural networks and ABC algorithm , 2016, Appl. Soft Comput..

[14]  Jiantao Zhou,et al.  Distribution of primary additional errors in fractal encoding method , 2014, Multimedia Tools and Applications.

[15]  Ilias Maglogiannis,et al.  Mobile healthcare information management utilizing Cloud Computing and Android OS , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[16]  Abdelkader Benyettou,et al.  Kernel-based learning and feature selection analysis for cancer diagnosis , 2017, Appl. Soft Comput..

[17]  Guo-Tan Liao,et al.  Hybrid classification engine for cardiac arrhythmia cloud service in elderly healthcare management , 2014, J. Vis. Lang. Comput..

[18]  Qihui Wu,et al.  Cellular-Base-Station-Assisted Device-to-Device Communications in TV White Space , 2015, IEEE Journal on Selected Areas in Communications.

[19]  Liuqing Yang,et al.  Securing physical-layer communications for cognitive radio networks , 2015, IEEE Communications Magazine.

[20]  Bilal Malik,et al.  Cloud Computing with Machine Learning Could Help Us in the Early Diagnosis of Breast Cancer , 2015, 2015 Second International Conference on Advances in Computing and Communication Engineering.

[21]  Wei Wang,et al.  The Controller Placement Problem in Software Defined Networking: A Survey , 2017, IEEE Network.

[22]  A. Jemal,et al.  Cancer statistics in China, 2015 , 2016, CA: a cancer journal for clinicians.

[23]  N. Dubrawsky.  Cancer statistics , 1989, CA: a cancer journal for clinicians.

[24]  Jingchao Li.  A New Robust Signal Recognition Approach Based on Holder Cloud Features under Varying SNR Environment , 2015, KSII Trans. Internet Inf. Syst..

[25]  Y. V. Lokeswari,et al.  A cloud-based data mining framework for improved clinical diagnosis through parallel classification , 2015, 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT).

[26]  Tao Jiang,et al.  Android Based Naive Bayes Probabilistic Detection Model for Breast Cancer and Mobile Cloud Computing: Design and Implementation , 2015 .

[27]  A. Jemal,et al.  Cancer treatment and survivorship statistics, 2016 , 2016, CA: a cancer journal for clinicians.

[28]  Xi He,et al.  Cloud Computing: a Perspective Study , 2010, New Generation Computing.