A Novel Internet of Things Framework Integrated with Real Time Monitoring for Intelligent Healthcare Environment
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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.
KeywordsInternet of things Body sensor network Neural network Decision trees Breast cancer Healthcare
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- 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
- 5.Peter, N., Enhancing random forest implementation in WEKA. In: Machine Learning Conference, 2005.Google Scholar
- 9.Mehta, M., Agrawal, R., and Rissanen, J., SLIQ: A scalable parallel classifier for data mining. IBM Almaden Research Center, CA 95120.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.CrossRefPubMedGoogle 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.
- 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.CrossRefGoogle Scholar