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A Novel Internet of Things Framework Integrated with Real Time Monitoring for Intelligent Healthcare Environment

  • Systems-Level Quality Improvement
  • Published:
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Abstract

During mammogram screening, there is a higher probability that detection of cancers is missed, and more than 16 percentage of breast cancer is not detected by radiologists. This problem can be solved by employing image processing algorithms which enhances the accuracy of the diagnostic through image segmentation which reduces the misclassified malignant cancers. By employing segmentation, the unnecessary regions in the breast close to the boundary between the breast tissue and segmented pectoral muscle can be removed, therefore enhancing the accuracy the calculation as well as feature estimation. In-order to enhance the accuracy of classification, the proposed classifier integrates the decision trees and neural network into a system to report the progress of the breast cancer patients in an appropriate manner with the help of technology used in healthcare system. The proposed classifier successfully demonstrated that it achieved more accurate prediction when compared with other widely used algorithms, namely, K-Nearest Neighbors, Support Vector Machine and Naive Bayes algorithm.

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References

  1. Magoulas, G. D., and Prentza, A., Machine learning in medical applications. In: Paliouras, G., Karkaletsis, V., Spyrpoulos, C. D. (Eds), Machine Learning and its Applications, Lecture Notes in Computer Science. Berlin: Springer-Verlag, 2010, 300–307.

    Google Scholar 

  2. Hsieh, S. L., Hsieh, S. H., Cheng, P. H. et al., Design ensemble machine learning model for breast cancer diagnosis. J. Med. Syst. 36:2841, 2012. https://doi.org/10.1007/s10916-011-9762-6.

    Article  PubMed  Google Scholar 

  3. Naghibi, S., Teshnehlab, M., and Shoorehdeli, M. A., Breast cancer classification based on advanced multi dimensional fuzzy neural network. J. Med. Syst. 36:2713, 2012. https://doi.org/10.1007/s10916-011-9747-5.

    Article  PubMed  Google Scholar 

  4. Murakami, Y., and Mizuguchi, K., Applying the nave Bayes classifier with kernel density estimation to the prediction of protein-protein interaction sites. Bioinformatics 26(15):1841–1848, 2010.

    Article  CAS  Google Scholar 

  5. Peter, N., Enhancing random forest implementation in WEKA. In: Machine Learning Conference, 2005.

  6. Levi, F., Bosetti, C., Lucchini, F., Negri, E., and La Vecchia, C., Monitoring the decrease in breast cancer mortality in Europe. Eur. J. Cancer Prev. 14(6):497–502, 2005.

    Article  Google Scholar 

  7. Tyczynski, J. E., Plesko, I., Aareleid, T., Primic-Zakelj, M., Dalmas, M., Kurtinaitis, J., Stengrevics, A., and Parkin, D. M., EU member states: Mortality declining in young women, but still increasing in the elderly. Int. J. Cancer 112(6):1056–1064, 2004.

    Article  CAS  Google Scholar 

  8. Saritas, I., Prediction of breast cancer using artificial neural networks. J. Med. Syst. 36:2901, 2012. https://doi.org/10.1007/s10916-011-9768-0.

    Article  PubMed  Google Scholar 

  9. Mehta, M., Agrawal, R., and Rissanen, J., SLIQ: A scalable parallel classifier for data mining. IBM Almaden Research Center, CA 95120.

  10. Li, J. B., Yu, Y., Yang, Z. M. et al., Breast tissue image classification based on semi-supervised locality discriminant projection with kernels. J. Med. Syst. 36:2779, 2012. https://doi.org/10.1007/s10916-011-9754-6.

    Article  PubMed  Google Scholar 

  11. Nassif, H., Page, D., Ayvaci, M., Shavlik, J., and Burnside, E. S., Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming. In: Veinot, T. (Ed.), Proceedings of the 1st ACM International Health Informatics Symposium (IHI ‘10). New York: ACM, 2010, 76–82.

    Google Scholar 

  12. Huang, M. L., Hung, Y. H., Lee, W. M. et al., Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis. J. Med. Syst. 36:407, 2012. https://doi.org/10.1007/s10916-010-9485-0.

    Article  PubMed  Google Scholar 

  13. Diz, J., Marreiros, G., and Freitas, A., Applying data mining techniques to improve breast cancer diagnosis. J. Med. Syst. 40(203), 2016. https://doi.org/10.1007/s10916-016-0561-y.

  14. Suresh, A., and Varatharajan, R., Recognition of pivotal instances from uneven set boundary during classification. Multimed. Tools Appl., 2018. https://doi.org/10.1007/s11042-018-5905-9.

    Article  Google Scholar 

  15. Issac Niwas, S., Palanisamy, P., Chibbar, R. et al., An expert support system for breast cancer diagnosis using color wavelet features. J. Med. Syst. 36:3091, 2012. https://doi.org/10.1007/s10916-011-9788-9.

    Article  CAS  PubMed  Google Scholar 

  16. Paulin, F., and Santhakumaran, A., Back propagation neural network by comparing hidden neurons: Case study on breast cancer diagnosis. Int. J. Comput. Appl. 2(4), 2010. (0975–8887).

    Article  Google Scholar 

  17. Ganatra, A., Panchal, G., Kosta, Y., and Gajjar, C., Initial classification through back propagation in a neural network following optimization through GA to evaluate the fitness of an algorithm. International Journal of Computer Science and Information Technology 3(1):98–116, 2011.

    Article  Google Scholar 

  18. Vapnik, V., and Vashist, A., A new learning paradigm: Learning using privileged information. Neural Netw. 22(5–6):544–557, 2009.

    Article  Google Scholar 

  19. Mahmoudabadi, H., Izadi, M., and Menhaj, M. B., A hybrid method for grade estimation using genetic algorithm and neural networks. Comput. Geosci. 13:91–101, 2009.

    Article  Google Scholar 

  20. Chattopadhyay, S., Kaur, P., Rabhi, F. et al., Neural network approaches to grade adult depression. J. Med. Syst. 36:2803, 2012. https://doi.org/10.1007/s10916-011-9759-1.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Nehru Institute of Engineering and Technology, T.M.Palayam, Coimbatore, TamilNadu, 641105, India

    A. Suresh

  2. Department of Computer Science and Engineering, Bharathidasan University, Trichy, Tiruchirappalli, India

    R. Udendhran & M. Balamurgan

  3. Department of Electronics and Communication Engineering, Sri Ramanujar Engineering College, Chennai, Tamil Nadu, 600127, India

    R. Varatharajan

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  1. A. Suresh
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  2. R. Udendhran
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  3. M. Balamurgan
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  4. R. Varatharajan
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Correspondence to A. Suresh.

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Suresh, A., Udendhran, R., Balamurgan, M. et al. A Novel Internet of Things Framework Integrated with Real Time Monitoring for Intelligent Healthcare Environment. J Med Syst 43, 165 (2019). https://doi.org/10.1007/s10916-019-1302-9

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