An Interactive and Predictive Pre-diagnostic Model for Healthcare based on Data Provenance
DOI:
https://doi.org/10.21928/uhdjst.v3n2y2019.pp59-73Keywords:
Hand Gesture Detection and Recognition, Pre-Diagnosis Disease, Data Provenance, Provenance Network Analytics, Machine Learning AlgorithmAbstract
The future of healthcare may look completely different from the current clinic-center services. Rapidly growing and developing technologies are expected to change clinics throughout the world. However, the healthcare delivered to impaired patients, such as elderly and disabled people, possibly still requires hands-on human expertise. The aim of this study is to propose a predictive model that pre-diagnose illnesses by analyzing symptoms that are interactively taken from patients via several hand gestures during a period of time. This is particularly helpful in assisting clinicians and doctors to gain better understanding and make more accurate decisions about future plans for their patients’ situations. The hand gestures are detected, the time of the gesture is recorded and then they are associated to their designated symptoms. This information is captured in the form of provenance graphs constructed based on the W3C PROV data model. The provenance graph is analyzed by extracting several network metrics and then supervised machine-learning algorithms are used to build a predictive model. The model is used to predict diseases from the symptoms with a maximum accuracy of 84.5%.
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[2] Y. Yang, Gesture Controlled user Interface for Elderly People, MSc Thesis, College of Applied Sciences, Oslo and Akershus University, 2016, published.
[3] World Health Organization, “World Report on Disability,” World Health Organization, 2011.
[4] W. Chen, “Gesture-Based Applications for Elderly People,” In International Conference on Human-Computer Interaction, Springer, Berlin, Heidelberg, pp. 186-195, July 2013.
[5] R. Orji, and K. Moffatt, “Persuasive Technology for Health and Wellness: State-of-the-Art and Emerging Trends,” Health Informatics Journal, vol. 24, no. 1, pp. 66-91, March 2018.
[6] S. Nowozin, P. Kohli, and J. D. Shotton, “Gesture Detection and Recognition,” U.S. Patent No. 9,619,035, April 2017.
[7] O. Asan, and E. Montague, “Technology-Mediated Information sharing between Patients and Clinicians in Primary Care Encounters,” Behaviour and Information Technology, vol. 33, no. 3, pp. 259- 270, March 2014.
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[10] L. Moreau and P. Missier, “PROV-DM: The PROV Data Model,” Tech. Rep. W3C Recommendation, W3C: http://www.w3.org/TR/prov- dm/, 2013.
[11] S. Xu, T. Rogers, E. Fairweather, A. Glenn, J. Curran, and V. Curcin, “Application of Data Provenance in Healthcare Analytics Software: Information Visualisation of User Activities,” AMIA Summits onTranslational Science Proceedings, pp. 263-272, 2018.
[12] T. D. Huynh, M. Ebden, J. Fischer, S. Roberts, and L. Moreau, “Provenance Network Analytics,” Data Mining and Knowledge Discovery, vol. 32, no. 3, pp.708-735, 2018.
[13] B. Shickel, P. J. Tighe, A. Bihorac, and P. Rashidi, “Deep EHR: a Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis,” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 5, pp. 1589-1604, October 2017.
[14] Choudhury, and D. Gupta, “A Survey on Medical Diagnosis of Diabetes Using Machine Learning Techniques,” In Recent Developments in Machine Learning and Data Analytics, Springer, Singapore, pp. 67-78, 2019.
[15] E. Nasr-Esfahani, N. Karimi, S. M. Soroushmehr, M. H. Jafari, M. A. Khorsandi, S. Samavi, and K. Najarian, “Hand Gesture Recognition for Contactless Device Control in Operating Rooms,” arXiv Preprint arXiv: 1611.04138, 2016.
[16] B. Roper, A. Chapman, D. Martin, and J. Morley, “A Graph Testing Framework for Provenance Network Analytics,” In International Provenance and Annotation Workshop, Springer, Cham, pp. 245-251, July 2018.
[17] T. Lebo, S. Sahoo, D. McGuinness, K. Belhajjame, J. Cheney, D. Corsar, D. Garijo, S. Soiland-Reyes, S. Zednik, and J. Zhao, “PROV- O: The PROV Ontology,” Tech. Rep., W3C Recommendation, W3C: http://www.w3.org/TR/prov-o/, 2013.
[18] H. Miao, and A. Deshpande, “Understanding Data Science Lifecycle Provenance via Graph Segmentation and Summarization,” arXiv preprint arXiv: 1810.04599, April 2018.
[19] G. Closa, J. Maso, B. Proß, and X. Pons, “W3C PROV to Describe Provenance at the Dataset, Feature and Attribute Levels in a Distributed Environment,” Computers, Environment and Urban Systems, vol. 64, no. 1, pp. 103-117, July 2017.
[20] J. Cheney, P. Missier, L. Moreau, T. DeNies, “Constraints of the PROV Data Model,” Tech. Rep., W3C Recommendation, W3C: http://www.w3.org/TR/prov-constraints/, 2013.
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[22] J. Hussein, L. Moreau, and V. Sassone, “Obscuring Provenance Confidential Information via Graph Transformation,” IFIP Advances in Information and Communication Technology, vol. 454, ISBN 978-3-319-18491-3, Springer International Publishing, pp. 109-125, May 2015.
[23] J. Hussein, and L. Moreau, “A Template-Based Graph Transformation System for the PROV Data Model,” In Seventh International Workshop on Graph Computation Models, 2016.
[24] L. Moreau, and P. Groth, “Provenance: An Introduction to PROV,” Synthesis Lectures on the Semantic Web: Theory and Technology, vol. 3, no. 4, pp. 1-129, September 2013.
[25] D. Anderson, Medical Terminology: The Best and Most Effective Way to Memorize, Pronounce and Understand Medical Terms. 2nd ed. US: Independently published, November 2016.
[26] A. Haria, A. Subramanian, N. Asokkumar, S. Poddar, and S. Nayak, “Hand Gesture Recognition for Human Computer Interaction,” Procedia Computer Science, vol. 115, pp. 367-374, January 2017.
[27] Portugal, P. Alencar, and D. Cowan, “The use of Machine Learning Algorithms in Recommender Systems: A Systematic Review,” Expert Systems with Applications, 97, pp. 205-227, May 2018.
[28] M. Arda, and D. Zeynep, “Sign-Language-Digits-Dataset,” Kaggle Dataset, 2017.
[29] F. Beser, A. Kizrak, B. Bolat, and T. Yildirim, “Recognition of Sign Language using Capsule Networks,” In 2018 26th Signal Processing and Communications Applications Conference (SIU), IEEE, pp. 1-4, 2018.
[30] F. Luus, N. Khan, and I. Akhalwaya, “Active Learning with Tensor- Board Projector,” arXiv preprint arXiv: 1901.00675. January 2019.
[31] M. Fatima, and M. Pasha, “Survey of Machine Learning Algorithms for Disease Diagnostic,” Journal of Intelligent Learning Systems and Applications, vol. 9, no. 1, pp. 1-16, 2017.
[32] L. Breiman, Classification and Regression Trees. New York, US: John Wiley & Sons, Inc, 2017.
[33] B. L. Deekshatulu, and P. Chandra, “Classification of Heart Disease using k-Nearest Neighbor and Genetic Algorithm,” Procedia Technology, vol. 10, no. 3, pp. 85-94, January 2013.
[34] Huang, G., Guo, C., Kusner, M.J., Sun, Y., Sha, F. and Weinberger, K.Q. “Supervised Word Mover’s Distance,” In Advances in Neural Information Processing Systems, pp. 4862-4870, 2016.
[35] S. Kumar, “Activity Recognition in Egocentric Video using SVM, KNN and Combined SVM and KNN Classifiers,” In IOP Conference Series: Materials Science and Engineering, vol. 225, no. 1, IOP Publishing, p. 12226, August 2017.
[36] B. J. Erickson, P. Korfiatis, Z. Akkus, and T. L. Kline, “Machine Learning for Medical Imaging,” Radiographics, vol. 37, no. 2, pp.505-515, February 2017.
[37] A. Yahyaoui, and N. Yumusak, “Decision Support System based on the Support Vector Machines and the Adaptive Support Vector Machines Algorithm for Solving Chest Disease Diagnosis Problems,” Biomedical Research, vol. 29, no. 7, pp.1474-1480, 2018.
[38] M. Pereda, and E. Estrada, “Machine Learning Analysis of Complex Networks in Hyperspherical Space,” arXiv preprint arXiv: 1804.05960, April 2018.
[39] K. Dobbin, and R. M. Simon, “Optimally Splitting Cases for Training and Testing High Dimensional Classifiers,” BMC Medical Genomics, vol. 4, no. 1, pp.31-39, December 2011.
[40] A. Shafique, and E. Hato, “Formation of Training and Testing Datasets, for Transportation Mode Identification,” Journal of Traffic and Logistics Engineering, vol. 3, no. 1, pp. 77-80, January 2015.
[41] K. M. Ting, “Confusion Matrix,” Encyclopedia of Machine Learning and Data Mining, Springer, Boston, MA, pp.260-260, April 2017.
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Published
2019-10-01
How to Cite
Ahmed, Z. N., & Hussien, J. A. (2019). An Interactive and Predictive Pre-diagnostic Model for Healthcare based on Data Provenance. UHD Journal of Science and Technology, 3(2), 59–73. https://doi.org/10.21928/uhdjst.v3n2y2019.pp59-73
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