Privacy Enhanced Healthcare Data Management Using Associative Data Mining Approaches

References

• Alapont, J., Bella-Sanjuán, A., Ferri, C., Hernández-Orallo, J., Llopis-Llopis, J. D., & Ramírez-Quintana, M. J., (2005). Specialised tools for automating data mining for hospital management. http://www.dsic.upv.es/∼abella/papers/HIS_DM.pdf.
   Google Scholar
• Ayyad, S. M., Saleh, A. I., & Labib, L. M. (2019). Gene expression cancer classification using modified K-Nearest neighbors technique. Bio Systems, 176, 41–51. https://doi.org/10.1016/j.biosystems.2018.12.009
   PubMed Web of Science ®Google Scholar
• Belciug, S., Gorunescu, F., Salem A., Gorunescu, M. (2010). Clustering-based approach for detecting breast cancer recurrence. 10th International Conference on Intelligent Systems Design and Applications.
   Google Scholar
• Ceglowski, R., Churilov, L., Wasserthiel, J. (2016). Combining data mining and discrete event simulation for a value-added view of a hospital emergency department. In N. Mustafee (Eds.), Operational research for emergency planning in healthcare: Volume 1. The OR essentials series. London: Palgrave Macmillan. https://doi.org/10.1057/9781137535696_6
   Google Scholar
• Hashim, N. W., Wilkes, M., Salomon, R., & Meggs, J. (2012). Analysis of timing pattern of speech as possible indicator for near-term suicidal risk and depression in male patients. 2012 International Conference on Signal Processing Systems (ICSPS 2012); 2012 Dec 21–22 (pp. 6–13). IACSIT Press.
   Google Scholar
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. In T. Hastie, R. Tibshirani, J. Friedman, editors. Unsupervised learning (2nd ed, pp. 485–585). Springer.
   Google Scholar
• Hill, T., & Lewicki, P. (2006). Statistics: Methods and applications: A comprehensive reference for science, industry, and data mining. StatSoft.
   Google Scholar
• Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. https://doi.org/10.1016/j.patrec.2009.09.011
   Web of Science ®Google Scholar
• Jia, X., & Meng, M. Q.-H. (2016). A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2016 Aug 16–20 (pp. 639–642). IEEE.
   Google Scholar
• Karegar, M., Isazadeh, A., Fartash, F., Saderi, T., & Navin, A. H. (2014). Data-mining by probability-based patterns. International Conference on Information Technology Interfaces; 2008 June 23–26 (pp. 353–360). IEEE.
   Google Scholar
• Karimi-Rouzbahani, H., & Daliri, M. (2011). Diagnosis of Parkinson’s disease in human using voice signals. Basic and Clinical Neuroscience, 2(3), 12–20.
   Google Scholar
• Kimes, P. K., Liu, Y., Neil Hayes, D., & Marron, J. S. (2017). Statistical significance for hierarchical clustering. Biometrics, 73(3), 811–821. https://doi.org/10.1111/biom.12647
   PubMed Web of Science ®Google Scholar
• Krishnaiah, V., Narsimha, G., & Chandra, D. N. S. (2013). Diagnosis of lung cancer prediction system using data mining classification techniques. International Journal of Computer Science & Information, 4(1), 39–45.
   Google Scholar
• MdO, R., Pereira, J. C., & Carvalho, A. (2002). Evaluation of neural classifiers using statistic methods for identification of laryngeal pathologies. Proceedings 5th Brazilian Symposium on Neural Networks (Cat No 98EX209); 1998 Dec 9–11. IEEE.
   Google Scholar
• Ng, K., Liu, H. (2000). Customer retention via data Mmining. Artificial Intelligence Review, 14, 569–590. https://doi.org/10.1023/A:1006676015154
   Google Scholar
• Nguyen, X. C., Le, H. B., & Cao, T. A. (2014). An enhanced scheme for privacy-preserving association rules mining on horizontally distributed databases. In Proceedings of IEEE International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF) (pp. 1–4). IEEE.
   Google Scholar
• Noyunsan, C., Katanyukul, T., & Saikaew, K. (2018). Performance evaluation of supervised learning algorithms with various training data sizes and missing attributes. Engineering and Applied Science Research, 45(3), 221–229.
   Google Scholar
• Pandit, H., & Shah, D. M. (2011). Application of digital image processing and analysis in healthcare based on medical palmistry (pp. 56–59). IJCA.
   Google Scholar
• Peng, C. Y., Lee, K. L., & Ingersoll, G. M. (2002). An introduction to logistic regression analysis and reporting. The Journal of Educational Research, 96(1), 3–14. https://doi.org/10.1080/00220670209598786
   Web of Science ®Google Scholar
• Pratumgul, W., & Sa-ngiamwibool, W. (2016). Classification of diabetic retinopathy using artificial neural network. Engineering and Applied Science Research, 43(1), 74–77.
   Google Scholar
• Premchaisawatt, S., & Ruangchaijatupon, N. (2016). Enhancing indoor positioning based on filter partitioning cascade machine learning models. Engineering and Applied Science Research, 43(3), 146–152.
   Google Scholar
• Sarraf, S., DeSouza, D. D., Anderson, J. A. E., Tofighi, G., & Deep, A. D. (2016). Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI. Bio Rxiv, pp. 1–6.
   Google Scholar
• Testik, M. C., Ozkaya, B. Y., Aksu, S. Ozcebe, O. I., (2012). Discovering blood donor arrival patterns using data mining: A method to investigate service quality at blood centers. Journal of Medical Systems, 36, 579–594. https://doi.org/10.1007/s10916-010-9519-7
   Google Scholar
• Thongkam, J., & Sukmak, V. (2017). Enhancing the performance of association rule models by filtering instances in colorectal cancer patients. Engineering and Applied Science Research, 44(2), 76–83.
   Google Scholar
• Thongkam, J., Sukmak, V., & Mayusiri, W. (2015). A comparison of regression analysis for predicting the daily number of anxiety-related outpatient visits with different time series data mining. Engineering and Applied Science Research, 42(3), 243–249.
   Google Scholar
• Verykios, V. S., & Gkoulalas-Divanis, A. (2008). A survey of association rule hiding methods for privacy. In Proceedings of privacy preserving data mining (pp. 267–289). Springer.
   Google Scholar
• Wimmer, G., Hegenbart, S., Vecsei, A., & Uhl, A. (2016). Convolutional neural network architectures for the automated diagnosis of celiac disease. In T. Peter (Ed.). Computer-assisted and robotic endoscopy (pp. 104–113). Springer.
   Google Scholar
• Wongtrairat, W., Namwet, P., & Pornnimitra, S. (2016). Early detection of Parkinson’s diseases by using the relationship between time response and movement characteristics of human arms. Engineering and Applied Science Research, 43(3), 127–134.
   Google Scholar
• Wu, X., Kumar, V., Ross Quinlan, J. Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zhou, Z. H., Steinbach, M., Hand D. J., Steinberg, D., (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14, 1–37. https://doi.org/10.1007/s10115-007-0114-2
   Google Scholar
• Zhang, R., Zhang, J., Zhang, Y., Sun, J., & Yan, G. (2013). Privacy-preserving profile matching for proximity-based mobile social networking. IEEE Journal on Selected Areas in Communications, 31, 656–668.
   Web of Science ®Google Scholar