A Recommendation System in the Medical Industry using SW-DBSCAN Algorithm

Document Type : Original Article


1 Department of Computer Engineering, Dezful branch, Islamic Azad University, Dezful, Iran

2 Department of Computer engineering Taras National University of Kyiv


A recommendation system is a system that, based on a limited amount of information provided by users as well as the feedback given to goods, persons, and locations by other users, provides appropriate suggestions to the user. Today, with the large number of physicians and specialists, it seems necessary to have a system for identifying the right specialist and experienced physician for the patient. We present in this study a system for medical recommendations that analyzes physicians and specialists. It uses collaborative filtering and scores provided by other users to suggest physician recommendations according to the area of expertise of the physician. Research conducted and evaluation of results show that this system can successfully recommend a specialist doctor to the user in 90% of cases.


  • Jianjia, H., Gang, L., Xiaojun, T., & Tingting, L. (2021). Research on collaborative recommendation of dynamic medical services based on cloud platforms in the industrial interconnection environment. Technological Forecasting and Social Change, 170, 120895.
  • Ponselvakumar, A. P., Anandamurugan, S., Logeswaran, K., Nivashini, S., Showentharya, S. K., & Jayashree, S. S. (2021, February). Advancement in precision medicine and recommendation system for clinical trials using deep learning methods. In IOP conference series: materials science and engineering (Vol. 1055, No. 1, p. 012110). IOP Publishing.
  • Zhang, M., Chen, Y., & Lin, J. (2021). A privacy-preserving optimization of neighborhood-based recommendation for medical-aided diagnosis and treatment. IEEE Internet of Things Journal, 8(13), 10830-10842.
  • Zhou, X., Li, Y., & Liang, W. (2020). CNN-RNN based intelligent recommendation for online medical pre-diagnosis support. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(3), 912-921.
  • Guzman, M. Torres-Ruiz, V. Tambonero, M. D. Lytras, B. López-Ramírez, R. Quintero, ... and W. Alhalabi, “A Collaborative Framework for Sensing Abnormal Heart Rate Based on a Recommender System: Semantic Recommender System for Healthcare”. J. Med. Biol. Eng., Vol. 38, No. 6, pp. 1026–1045, 2018.
  • G. D. Ochoa, O. Csiszár, and T. Schimper, “Medical recommender systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks”. BMC medical informatics and decision making, Vol. 21, No. 1, pp. 1-15, 2021.
  • Rustam, Z. Imtiaz, A. Mehmood, V. Rupapara, G. S. Choi, S. Din, and I. Ashraf, “Automated disease diagnosis and precaution recommender system using supervised machine learning”. Multimed Tools Appl., Vol. 81, pp. 31929–31952, 2022.
  • P. Erdeniz, A. Menychtas, I. Maglogiannis, A. Felfernig, and T. N. T. Tran, “Recommender systems for IoT enabled quantified-self applications”. Evolving Systems, Vol. 11, pp.291–304, 2020.
  • Hung, J. Xu, E. Lauren, M. W. Voss, M. N. Rosales, W. Su, ... and F. W. Licari, “Development of a recommender system for dental care using machine learning”. SN Appl. Sci., Vol. 1, p. 785, 2019.
  • Dhelim, N. Aung, M. A. Bouras, H. Ning, and E. Cambria, “A survey on personality-aware recommendation systems”. Artificial Intelligence Review, Vol. 55, No. 3, pp. 2409-2454, 2022..‏
  • Ge, S. Liu, Z. Fu, J. Tan, Z. Li, S. Xu, ... and Y. Zhang, “A survey on trustworthy recommender systems”. arXiv preprint arXiv:2207.12515., 2022.
  • H. Khan, J. Siddqui, and S. S. Sohail, “A Survey of Recommender Systems Based on Semi-supervised Learning”. In International Conference on Innovative Computing and Communications, Springer, Singapore, 2022.‏
  • [13] Alhijawi, Y. Kilani, “The recommender system: a survey”. Int. J. Adv. Intell. Paradigms, Vol. 15, No. 3, pp. 229-251, 2020..‏
  • K. Gupta, and P. Chandra, “A comprehensive survey of data mining”. International Journal of Information Technology, Vol. 12, No. 4, 1243-1257, 2020.‏
  • S. Ageed, S. R. Zeebaree, M. M. Sadeeq, S. F. Kak, H. S. Yahia, M. R. Mahmood, and I. M. Ibrahim, “Comprehensive survey of big data mining approaches in cloud systems”. Qubahan Academic Journal, Vol. 1, No. 2, pp. 29-38, 2021.‏
  • K. Ibrahim, and A. J. Obaid, “Web Mining Techniques and Technologies: A Landscape View”. In Journal of Physics: Conference Series, IOP Publishing, May 2021, Vol. 1879, No. 3, p. 032125.‏
  • S. Tandel, A. Jamadar, and S. Dudugu, “A survey on text mining techniques”. In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), IEEE, March 2019, pp. 1022-1026.‏
  • Bhattacharjee, and P. Mitra, “A survey of density based clustering algorithms”. Frontiers of Computer Science, Vol. 15, No. 1, pp. 1-27, 2021.
  • Ren, J. Zhang, L. Khoukhi, H. Labiod, and V. Vèque, “A review of clustering algorithms in VANETs”. Annals of Telecommunications, Vol. 76, No. 9, pp. 581-603, 2021.‏
  • A. N. Alexandropoulos, S. B. Kotsiantis, and M. N. Vrahatis, “Data preprocessing in predictive data mining”. The Knowledge Engineering Review, Vol. 34, 2019.
  • Ohadi, A. Kamandi, M. Shabankhah, S. M. Fatemi, S. M. Hosseini, and A. Mahmoudi, “SW-DBSCAN: A grid-based DBSCAN algorithm for large datasets”. In 2020 6th International Conference on Web Research (ICWR), IEEE, April 2020, pp. 139-145.
  •  Reza Molaee Fard received his MSc degree in 2020 from Islamic Azad University, Dezful Branch. His research interests include data mining, Recommender systems, and wireless sensor networks, and he has conducted research in these areas.
  •  Payam Yarahmadi received his M.SC in 2009 from Taras University of Ukraine and received his P.HD in 2015 from Taras University of Ukraine. His favorite research field is data mining, web mining and machine learning.