Doctor Recommendation Based on Patient Syndrome Using Convolutional Neural Network
DOI:
https://doi.org/10.46603/ejcee.v2i1.36Keywords:
Health Recommendation System , Content Based Filtering, CNN, Deep LearningAbstract
Recommendation systems in the online medical sector assist patients in finding appropriate doctors. This paper aimed to solve the complication in doctors' recommendations, concerning that people often struggle to see sure doctors according to their medical needs. Currently, most existing systems create doctors' recommendations through explicit or implicit feedback mechanisms. This doctor recommendation model does not depend on user feedback; instead, candidate doctors are generated for guidance solely from the user's current medical conditions. A prognosis is predicted for a specific syndrome via CNN. By applying discrete rules, the system identifies and fetches the most relevant specialists according to the prediction and provides necessary information. The performance evaluation results of the proposed method are high and satisfactory.
Downloads
References
S. J. Gong, "Personalized recommendation system based on association rules mining and collaborative filtering" , Applied Mechanics and Materials, Vol. 39. Trans Tech Publications Ltd, 2011.
L. Chen, R. Nayak, and Y. Xu, "A recommendation method for online dating networks based on social relations and demographic information." 2011 International Conference on Advances in Social Networks Analysis and Mining. IEEE, 2011.
M. Kompan, and M. Bieliková, "Content-based news recommendation." International conference on electronic commerce and web technologies. Springer, Berlin, Heidelberg, 2010.
A. L. Deng, Y. Y. Zhu, and B. Shi, "A collaborative filtering recommendation algorithm based on item rating prediction," Journal of Software, vol.14, no. 9, pp. 1621-1628, 2003.
C. Huang, J. Yin, and J. Wang. "Collaborative filtering recommendation algorithm for uncertain nearest neighbor," Journal of Computer Science, vol. 33, no. 8, pp. 1369-1377, 2010.
J. Bobadilla, A. Hernando, F. Ortega, and J. Bernal, 2011. A framework for collaborative filtering recommender systems. Expert Systems with Applications, vol. 38, no. 12, pp.14609-14623, 2011.
K. Chen, P. Han, J. Wu, "Recommendation algorithm for heterogeneous social networks based on user clustering," Journal of Computer Science, vol. 36, no. 2, pp. 2-5,(Chinese), 2013.
K. L. Cheung, D. Durusu, X. Sui, and H. de Vries, "How Recommender Systems Could Support and Enhance Computer-Tailored Digital Health Programs: A Scoping Review." Digital Health, vol. 5, pp. 1-19, 2019.
H. Schafer, S. Hor-Fraile, and R. P. Kumar, A. C. Valdez, A. Said, H. Torkamaan, T. Ulmer and C. Trattner. "Towards health (aware) recommender systems," In Proceedings of the International conference on digital health, pp. 157-161, 2017, London.
A. S. Hussein, W. M. Omar, X. Li and M. Ati, "Efficient Chronic Disease Diagnosis Prediction and Recommendation System." In IEEE-EMBS Conference on Biomedical Engineering and Sciences, pp. 209-214, IEEE, 2012, doi:10.1109/IECBES.2012.6498117.
M. Wiesner and D. Pfeifer, "Adapting Recommender Systems to the Requirements of Personal Health Record Systems." In Proceedings of the 1st ACM International Health Informatics Symposium, pp. 410–414, 2010. doi:10.1145/1882992.1883053.
F. Narducci, C. Musto, M. Polignano, M. de Gemmis, P. Lops and G. Semeraro, "A Recommender System for 14 M. WAQAR ET AL. Connecting Patients to the Right Doctors in the Health Net Social Network." In Proceedings of the 24th International Conference on World Wide Web, pp. 81–82, 2015. doi:10.1145/2740908.2742748.
A. Salunke, B. Kasar, and L. Smita,"Personalized Recommendation System for Medical Assistance Using Hybrid Filtering," International Journal of Computer Applications, vol.128, no. 9, pp. 6–10, 2015.
J. Blue,"Healthcare similarity engine," U.S. Patent No. 10,127,359. 13 Nov. 2018.
C. Migena, Z. Orhan, and E. Domnori. "e-Medical Test Recommendation System Based on the Analysis of Patients' Symptoms and Anamneses," Part of the IFMBE Proceedings book series, vol. 62, March 15, 2017.
W. Chen, and M. Xu, "The research of doctors recommendation algorithm based on clustering and collaborative filtering," Proceedings of the 23rd International Conference on Industrial Engineering and Engineering Management , Atlantis Press, Paris, 2017.
J. Bobadilla, F. Serradilla, and A. Hernando, "Collaborative filtering adapted to recommender systems of e-learning," Knowledge-Based Systems, vol. 22, no. 4, pp. 261 – 265, 2009.
D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, "Using collaborative filtering to weave an information tapestry," Commun. ACM, vol. 35, pp. 61–70, 1992.
J. Bobadilla, F. Ortega, A. Hernando, and A. Guti'errez, "Recommender systems survey," Knowledge-Based Systems, vol. 46, pp. 109 – 132, 2013.
G. Adomavicius and A. Tuzhilin, "Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 2005.
X. Zhou, Y. Xu, Y. Li, A. Josang, and C. Cox, "The state-of-the-art in personalized recommender systems for social networking," Artificial Intelligence Review, vol. 37, no. 2, pp. 119–132, 2012.
T. T. Contributors, "What is convolutional neural network? - Definition from WhatIs.com," Search Enterprise AI, 26-Apr-2018. [Online]. Available: https://searchenterpriseai.techtarget.com/definition/convolutional-neural-network. [Accessed: 24-Jan-2021].
"Keras Conv1D: Working with 1D Convolutional Neural Networks in Keras," MissingLink.ai. [Online]. Available: https://missinglink.ai/guides/keras/keras-conv1d-working-1d-convolutional-neural-networks-keras/. [Accessed: 24-Jan-2021].
"Max Pooling in Convolutional Neural Networks explained," deeplizard. [Online]. Available: https://deeplizard.com/learn/video/ZjM_XQa5s6s. [Accessed: 31-Mar-2021].
"Top 10 Reasons Why Python is So Popular With Developers in 2021," upGrad blog, 03-Apr-2021. [Online]. Available: https://www.upgrad.com/blog/reasons-why-python-popular-with-developers/. [Accessed: 09-May-2021].
Simplilearn, "What is Tensorflow: Deep Learning Libraries and Program Elements Explained," Simplilearn.com, 24-Mar-2021. [Online]. Available: https://www.simplilearn.com/tutorials/deep-learning-tutorial/what-is-tensorflow. [Accessed: 09-May-2021].
J. Brownlee, "Your First Deep Learning Project in Python with Keras Step-By-Step," Machine Learning Mastery, 15-Sep-2020. [Online]. Available: https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/. [Accessed: 09-May-2021].
R. M, “What Is Matplotlib In Python? How to use it for plotting?," ActiveState, 08-Apr-2021. [Online]. Available: https://www.activestate.com/resources/quick-reads/what-is-matplotlib-in-python-how-to-use-it-for-plotting/. [Accessed: 09-May-2021].
anujdutt9, “anujdutt9/Disease-Prediction-from-Symptoms,” GitHub. [Online]. Available: https://github.com/anujdutt9/Disease-Prediction-from-Symptoms/blob/master/dataset/test_data.csv. [Accessed: 09-May-2021].
"Specialist Doctor List in Chittagong - Doctors in Chattogram," Doctor Bangladesh, 05-Apr-2021. [Online]. Available: https://www.doctorbangladesh.com/doctors-chittagong/. [Accessed: 22-Apr-2021].
“Confusion matrix in machine learning,” 21-Aug-2020. [Online]. Available: https://www.geeksforgeeks.org/confusion-matrix-machine-learning/. [Accessed: 03-May-2021].
L. Sulieman, D. Gilmore, C. French, R. Cronin, G. Jackson, M. Russell and D. Fabbri, “Classifying Patient Portal Messages Using Convolutional Neural Networks,” Journal of Biomedical Informatics, vol. 74, pp. 59-70, 2017. Doi: 10.1016/j.jbi.2017.08.014.
C. Yao, Y. Qu, L. Guo, L. Chao, W. Cui, and L. Feng," A Convolutional Neural Network Model for Online Medical Guidance", IEEE Access, vol. 4, pp. 4094-4103, 2016. Doi:10.1109/ACCESS.2016.2594839.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Promila Haque, Soumik, Sherra
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
