A Hybrid Semi-Supervised Approach for Estimating the Efficient and Optimal Level of Hospitals Outputs

References

• Al-Rammahi, A. A., S. Farah, and F. G. Al-Jelaihawi. 2020. Covid-19 plasma monitoring based on clustering a large set of recovered patient data. In 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), October, 1–4. doi:10.1109/FarEastCon50210.2020.9271296.
   Google Scholar
• Ali, F., S. El-Sappagh, S. R. Islam, D. Kwak, A. Ali, M. Imran, and K. S. Kwak. 2020. A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Information Fusion 63:208–22. doi:10.1016/j.inffus.2020.06.008.
   Web of Science ®Google Scholar
• Askari, S., N. Montazerin, M. F. Zarandi, and E. Hakimi. 2017. Generalized entropy based possibilistic fuzzy C-Means for clustering noisy data and its convergence proof. Neurocomputing 219:186–202. doi:10.1016/j.neucom.2016.09.025.
   Web of Science ®Google Scholar
• Azadeh, A., S. M. Haghighi, Z. Gaeini, and N. Shabanpour. 2016. Optimization of healthcare supply chain in context of macro-ergonomics factors by a unique mathematical programming approach. Applied Ergonomics 55:46–55. doi:10.1016/j.apergo.2016.01.002.
   PubMed Web of Science ®Google Scholar
• Banker, R. D., A. Charnes, and W. W. Cooper. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science 30 (9):1078–92. doi:10.1287/mnsc.30.9.1078.
   Web of Science ®Google Scholar
• Bautista, J. R., Y. Zhang, and J. Gwizdka. 2022. Predicting healthcare professionals’ intention to correct health misinformation on social media. Telematics and Informatics 101864.
   Google Scholar
• Caballer-Tarazona, M., I. Moya-Clemente, D. Vivas-Consuelo, and I. Barrachina-Martínez. 2010. A model to measure the efficiency of hospital performance. Mathematical and Computer Modelling 52 (7–8):1095–102. doi:10.1016/j.mcm.2010.03.006.
   Google Scholar
• Casalino, G., M. Dominiak, F. Galetta, and K. Kaczmarek-Majer. 2020. Incremental semi-supervised fuzzy C-Means for bipolar disorder episode prediction. In 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), May, 1–8. doi:10.1109/EAIS48028.2020.9122748.
   Google Scholar
• Cao, S. J., J. Ding, and C. Ren. 2020. Sensor deployment strategy using cluster analysis of Fuzzy C-Means Algorithm: Towards online control of indoor environment’s safety and health. Sustainable Cities and Society 59:102190. doi:10.1016/j.scs.2020.102190.
   Web of Science ®Google Scholar
• Charnes, A., W. W. Cooper, and E. Rhodes. 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2 (6):429–44. doi:10.1016/0377-2217(78)90138-8.
   Web of Science ®Google Scholar
• Chowdhury, H., and V. Zelenyuk. 2016. Performance of hospital services in Ontario: DEA with truncated regression approach. Omega 63:111–22. doi:10.1016/j.omega.2015.10.007.
   Web of Science ®Google Scholar
• Chen, H., S. Das, J. M. Morgan, and K. Maharatna. 2022. Prediction and classification of ventricular arrhythmia based on phase-space reconstruction and fuzzy c-means clustering. Computers in Biology and Medicine 142:105180.
   PubMed Web of Science ®Google Scholar
• Dadkhah, M., M. Jahangoshai-Rezaee, and A. Z. Chavoshi. 2018. Short-term power output forecasting of hourly operation in power plant based on climate factors and effects of wind direction and wind speed. Energy 148:775–88. doi:10.1016/j.energy.2018.01.163.
   Web of Science ®Google Scholar
• de Barros Franco, D. G., and M. T. A. Steiner. 2018. Clustering of solar energy facilities using a hybrid fuzzy c-means algorithm initialized by metaheuristics. Journal of Cleaner Production 191:445–57. doi:10.1016/j.jclepro.2018.04.207.
   Web of Science ®Google Scholar
• de Oliveira, L. S., S. B. Gruetzmacher, and J. P. Teixeira. 2021. COVID-19 time series prediction. Procedia Computer Science 181:973–80. doi:10.1016/j.procs.2021.01.254.
   Google Scholar
• Dotoli, M., N. Epicoco, M. Falagario, and F. Sciancalepore. 2015. A cross-efficiency fuzzy data envelopment analysis technique for performance evaluation of decision-making units under uncertainty. Computers & Industrial Engineering 79:103–14. doi:10.1016/j.cie.2014.10.026.
   Web of Science ®Google Scholar
• El Zein, O. M., L. M. El Bakrawy, and N. I. Ghali. 2017. A robust 3D mesh watermarking algorithm utilizing fuzzy C-Means clustering. Future Computing and Informatics Journal 2 (2):148–56. doi:10.1016/j.fcij.2017.10.007.
   Google Scholar
• Gholami, R., D. A. Higón, and A. Emrouznejad. 2015. Hospital performance: Efficiency or quality? Can we have both with IT? Expert Systems with Applications 42 (12):5390–400. doi:10.1016/j.eswa.2014.12.019.
   Web of Science ®Google Scholar
• Guo, I. L., H. S. Lee, and D. Lee. 2017. An integrated model for slack-based measure of super-efficiency in additive DEA. Omega 67:160–7. doi:10.1016/j.omega.2016.05.002.
   Web of Science ®Google Scholar
• Jahangoshai-Rezaee, M., M. Jozmaleki, and M. Valipour. 2018. Integrating dynamic fuzzy C-means, data envelopment analysis, and artificial neural network to online prediction performance of companies in stock exchange. Physica A: Statistical Mechanics and Its Applications 489:78–93. doi:10.1016/j.physa.2017.07.017.
   Web of Science ®Google Scholar
• Jahangoshai Rezaee, M., A. Karimdadi, and H. Izadbakhsh. 2019. Road map for progress and attractiveness of Iranian hospitals by integrating self-organizing map and context-dependent DEA. Health Care Management Science 22 (3):410–36. doi:10.1007/s10729-019-09484-2.
   PubMed Web of Science ®Google Scholar
• Jahangoshai Rezaee, M., A. Moini, and F. Haji-Ali Asgari. 2012. Unified performance evaluation of health centers with integrated model of data envelopment analysis and bargaining game. Journal of Medical Systems 36 (6):3805–15. doi:10.1007/s10916-012-9853-z.
   PubMed Web of Science ®Google Scholar
• Jahangoshai Rezaee, M., S. Yousefi, and J. Hayati. 2018. A decision system using fuzzy cognitive map and multi-group data envelopment analysis to estimate hospitals’ outputs level. Neural Computing and Applications 29 (3):761–77. doi:10.1007/s00521-016-2478-2.
   Web of Science ®Google Scholar
• Kalantari, A., A. Kamsin, S. Shamshirband, A. Gani, H. Alinejad-Rokny, and A. T. Chronopoulos. 2018. Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges, and research directions. Neurocomputing 276:2–22. doi:10.1016/j.neucom.2017.01.126.
   Web of Science ®Google Scholar
• Kennedy, J., and R. Eberhart. 1995. PSO optimization. Vol. 27, 1941–8. Piscataway, NJ: IEEE Service Center.
   Google Scholar
• Kesemen, O., Ö. Tezel, and E. Özkul. 2016. Fuzzy c-means clustering algorithm for directional data (FCM4DD). Expert Systems with Applications 58:76–82. doi:10.1016/j.eswa.2016.03.034.
   Web of Science ®Google Scholar
• Koohi, H., and K. Kiani. 2016. User based collaborative filtering using fuzzy C-means. Measurement 91:134–9. doi:10.1016/j.measurement.2016.05.058.
   Web of Science ®Google Scholar
• Kumar, N, and H. Kumar. 2022. A novel hybrid fuzzy time series model for prediction of COVID-19 infected cases and deaths in India. ISA Transactions 124:69–81.
   PubMed Web of Science ®Google Scholar
• Kusy, M. 2018. Fuzzy c-means-based architecture reduction of a probabilistic neural network. Neural Networks 108:20–32. doi:10.1016/j.neunet.2018.07.012.
   PubMed Web of Science ®Google Scholar
• Kwon, H. B., J. H. Marvel, and J. J. Roh. 2017. Three-stage performance modeling using DEA–BPNN for better practice benchmarking. Expert Systems with Applications 71:429–41. doi:10.1016/j.eswa.2016.11.009.
   Web of Science ®Google Scholar
• Mahalle, P. N., N. P. Sable, N. P. Mahalle, and G. R. Shinde. 2020. Data analytics: Covid-19 prediction using multimodal data. In Intelligent Systems and Methods to Combat Covid-19, edited by A. Joshi, N. Dey, and K. Santosh, 1–10. Singapore: Springer. doi:10.1007/978-981-15-6572-4_1.
   Google Scholar
• Miao, J., X. Zhou, and T. Z. Huang. 2020. Local segmentation of images using an improved fuzzy C-means clustering algorithm based on self-adaptive dictionary learning. Applied Soft Computing 91:106200. doi:10.1016/j.asoc.2020.106200.
   Web of Science ®Google Scholar
• Mohmed, G., A. Lotfi, C. Langensiepen, and A. Pourabdollah. 2018. Clustering-based fuzzy finite state machine for human activity recognition. In UK Workshop on Computational Intelligence, September, 264–75. Cham: Springer.
   Google Scholar
• Muthu, B., C. B. Sivaparthipan, G. Manogaran, R. Sundarasekar, S. Kadry, A. Shanthini, and A. Dasel. 2020. IOT based wearable sensor for diseases prediction and symptom analysis in healthcare sector. Peer-to-Peer Networking and Applications 13 (6):2123–34. doi:10.1007/s12083-019-00823-2.
   Web of Science ®Google Scholar
• Otay, I., B. Oztaysi, S. C. Onar, and C. Kahraman. 2017. Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP&DEA methodology. Knowledge-Based Systems 133:90–106. doi:10.1016/j.knosys.2017.06.028.
   Web of Science ®Google Scholar
• Pimentel, B. A., and R. M. de Souza. 2016. Multivariate Fuzzy C-Means algorithms with weighting. Neurocomputing 174:946–65. doi:10.1016/j.neucom.2015.10.011.
   Web of Science ®Google Scholar
• Prabha, S., and C. M. Sujatha. 2018. Proposal of index to estimate breast similarities in thermograms using fuzzy C means and anisotropic diffusion filter based fuzzy C means clustering. Infrared Physics & Technology 93:316–25. doi:10.1016/j.infrared.2018.08.018.
   Web of Science ®Google Scholar
• Rezaee, M. J., and M. Dadkhah. 2019. A hybrid approach based on inverse neural network to determine optimal level of energy consumption in electrical power generation. Computers & Industrial Engineering 134:52–63. doi:10.1016/j.cie.2019.05.024.
   Web of Science ®Google Scholar
• Rezaee, M. J., M. Dadkhah, and M. Falahinia. 2019. Integrating neuro-fuzzy system and evolutionary optimization algorithms for short-term power generation forecasting. International Journal of Energy Sector Management 13 (4):828–45.
   Google Scholar
• Rezaee, M. J., and A. Karimdadi. 2015. Do geographical locations affect in hospitals performance? A multi-group data envelopment analysis. Journal of Medical Systems 39 (9):85. doi:10.1007/s10916-015-0278-3.
   PubMed Web of Science ®Google Scholar
• Sefidian, A. M., and N. Daneshpour. 2019. Missing value imputation using a novel grey based fuzzy c-means, mutual information based feature selection, and regression model. Expert Systems with Applications 115:68–94. doi:10.1016/j.eswa.2018.07.057.
   Web of Science ®Google Scholar
• Sen, S., and A. V. Deokar. 2022. Toward understanding variations in price and billing in US healthcare services: A predictive analytics approach. Expert Systems with Applications 118241.
   Google Scholar
• Shabanpour, H., S. Yousefi, and R. F. Saen. 2017. Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks. Journal of Cleaner Production 142:1098–107. doi:10.1016/j.jclepro.2016.08.147.
   Web of Science ®Google Scholar
• Singh, C., and A. Bala. 2018. A DCT-based local and non-local fuzzy C-means algorithm for segmentation of brain magnetic resonance images. Applied Soft Computing 68:447–57. doi:10.1016/j.asoc.2018.03.054.
   Web of Science ®Google Scholar
• Sowan, B., K. Dahal, M. A. Hossain, L. Zhang, and L. Spencer. 2013. Fuzzy association rule mining approaches for enhancing prediction performance. Expert Systems with Applications 40 (17):6928–37. doi:10.1016/j.eswa.2013.06.025.
   Web of Science ®Google Scholar
• Suleman, A. 2017. Measuring the congruence of fuzzy partitions in fuzzy c-means clustering. Applied Soft Computing 52:1285–95. doi:10.1016/j.asoc.2016.06.037.
   Web of Science ®Google Scholar
• Suresh, A., K. V. Harish, and N. Radhika. 2015. Particle swarm optimization over back propagation neural network for length of stay prediction. Procedia Computer Science 46:268–75. doi:10.1016/j.procs.2015.02.020.
   Google Scholar
• Tóth, B., and J. Vad. 2018. A fuzzy clustering method for periodic data, applied for processing turbomachinery beamforming maps. Journal of Sound and Vibration 434:298–313. doi:10.1016/j.jsv.2018.08.002.
   Web of Science ®Google Scholar
• Truong, H. Q., L. T. Ngo, and W. Pedrycz. 2017. Granular fuzzy possibilistic C-means clustering approach to DNA microarray problem. Knowledge-Based Systems 133:53–65. doi:10.1016/j.knosys.2017.06.019.
   Web of Science ®Google Scholar
• Vimont, A., H. Leleu, and I. Durand-Zaleski. 2022. Machine learning versus regression modelling in predicting individual healthcare costs from a representative sample of the nationwide claims database in France. The European Journal of Health Economics 23 (2):211–23.
   PubMed Web of Science ®Google Scholar
• Wang, B., Y. Tan, and W. Jia. 2022. TL-FCM: A hierarchical prediction model based on two-level fuzzy c-means clustering for bike-sharing system. Applied Intelligence 52 (6):6432–49. doi:10.1007/s10489-021-02186-9.
   Web of Science ®Google Scholar
• Yang, M. S., and Y. Nataliani. 2017. Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters. Pattern Recognition 71:45–59. doi:10.1016/j.patcog.2017.05.017.
   Web of Science ®Google Scholar
• Yiqing, L., Y. Xigang, and L. Yongjian. 2007. An improved PSO algorithm for solving non-convex NLP/MINLP problems with equality constraints. Computers & Chemical Engineering 31 (3):153–62. doi:10.1016/j.compchemeng.2006.05.016.
   Web of Science ®Google Scholar
• Yuvaraj, N., and K. R. SriPreethaa. 2019. Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster. Cluster Computing 22 (S1):1–9. doi:10.1007/s10586-017-1532-x.
   Google Scholar
• Zainuddin, Z., and O. Pauline. 2015. An effective fuzzy C-means algorithm based on symmetry similarity approach. Applied Soft Computing 35:433–48. doi:10.1016/j.asoc.2015.06.021.
   Web of Science ®Google Scholar
• Zare, M., and M. Koch. 2018. Groundwater level fluctuations simulation and prediction by ANFIS-and hybrid Wavelet-ANFIS/Fuzzy C-Means (FCM) clustering models: Application to the Miandarband plain. Journal of Hydro-Environment Research 18:63–76. doi:10.1016/j.jher.2017.11.004.
   Web of Science ®Google Scholar
• Zhou, J., W. Pedrycz, X. Yue, C. Gao, Z. Lai, and J. Wan. 2021. Projected fuzzy C-means clustering with locality preservation. Pattern Recognition 113:107748. doi:10.1016/j.patcog.2020.107748.
   Web of Science ®Google Scholar