| Sonstiges: |
- Nachgewiesen in: MEDLINE
- Sprachen: English
- Publication Type: Journal Article
- Language: English
- [Environ Sci Pollut Res Int] 2024 Jun; Vol. 31 (26), pp. 38343-38357. <i>Date of Electronic Publication: </i>2024 May 27.
- MeSH Terms: Machine Learning* ; Medical Waste* ; Bahrain ; Humans
- References: Abbasi M, El Hanandeh A (2016) Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Manage 56:13–22. (PMID: 10.1016/j.wasman.2016.05.018) ; Abbasi M, Abduli M, Omidvar B, Baghvand A (2013) Forecasting municipal solid waste generation by hybrid support vector machine and partial least square model. Int J Environ Res 7(1):27–38. ; Adamović VM, Antanasijević DZ, Ristić MĐ, Perić-Grujić AA, Pocajt VV (2018) An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level. J Mater Cycles Waste Manage 20(3):1736–1750. (PMID: 10.1007/s10163-018-0741-6) ; Ahmad M, Kamiński P, Olczak P, Alam M, Iqbal MJ, Ahmad F, Khan BJ (2021) Development of prediction models for shear strength of rockfill material using machine learning techniques. Appl Sci 11(13):6167. (PMID: 10.3390/app11136167) ; Alhumoud JM, Alhumoud HM (2007) An analysis of trends related to hospital solid wastes management in Kuwait. Manag Environ Qual 18(5):502–513. https://doi.org/10.1108/14777830710778274. (PMID: 10.1108/14777830710778274) ; Ali Abdoli M, Falah Nezhad M, Salehi Sede R, Behboudian S (2012) Longterm forecasting of solid waste generation by the artificial neural networks. Environ Prog Sustainable Energy 31(4):628–636. (PMID: 10.1002/ep.10591) ; Al-Omran K, Khan E, Ali N, Bilal M (2021) Estimation of COVID-19 generated medical waste in the Kingdom of Bahrain. Sci Total Environ 801:149642. (PMID: 10.1016/j.scitotenv.2021.149642) ; Al-Omran K, Abahussain A, Khan E (2023a) Integrated environmental assessment of medical waste management in the Kingdom of Bahrain. Sustainability 15(3):2397. (PMID: 10.3390/su15032397) ; Al-Omran K, Khan E, Perna S, Ali N (2023b) Factors associated with medical waste under pandemic situation: a case study of the Kingdom of Bahrain. J Mater Cycles Waste Manag 25(5):2951–2963. (PMID: 10.1007/s10163-023-01728-2) ; Al-Shallash, K., & Shereif, M. (2007). Healthcare waste management in Saudi Arabia. A case study. Paper presented at the Proceedings of the 10th International Conference on Environmental Science and Technology, Kos island, Greece, September 5–7, 2007, 24 - 27. ; Altin FG, Budak İ, Özcan F (2023) Predicting the amount of medical waste using kernel-based SVM and deep learning methods for a private hospital in Turkey. Sustain Chem Pharm 33:101060. (PMID: 10.1016/j.scp.2023.101060) ; Arabgol S, Ko HS (2013) Application of artificial neural network and genetic algorithm to healthcarewaste prediction. J Artif Intell Soft Comput Res 3(4):243–250. (PMID: 10.2478/jaiscr-2014-0017) ; Basith S, Manavalan B, Shin TH, Lee G (2018) iGHBP: computational identification of growth hormone binding proteins from sequences using extremely randomised tree. Comput Struct Biotechnol J 16:412–420. (PMID: 10.1016/j.csbj.2018.10.007) ; Bdour A, Altrabsheh B, Hadadin N, Al-Shareif M (2007) Assessment of medical wastes management practice: a case study of the northern part of Jordan. Waste Manage 27(6):746–759. (PMID: 10.1016/j.wasman.2006.03.004) ; Breiman L (1996) Bagging predictors. Mach Learn 24:123–140. (PMID: 10.1007/BF00058655) ; Byeon H (2021) Exploring factors for predicting anxiety disorders of the elderly living alone in South Korea using interpretable machine learning: a population-based study. Int J Environ Res Public Health 18(14):7625. (PMID: 10.3390/ijerph18147625) ; Carbonell JG, Michalski RS, Mitchell TM (1983) An overview of machine learning. Mach Learn 1:3–23. https://doi.org/10.1016/B978-0-08-051054-5.50005-4. (PMID: 10.1016/B978-0-08-051054-5.50005-4) ; CBB. (2023). Publications & data-financial stability report. Retrieved from https://www.cbb.gov.bh/publications/. ; Ceylan Z, Bulkan S, Elevli S (2020) Prediction of medical waste generation using SVR, GM (1, 1) and ARIMA models: a case study for megacity Istanbul. J Environ Health Sci Eng 18(2):687–697. (PMID: 10.1007/s40201-020-00495-8) ; Chakraborty D, Mondal J, Barua HB, Bhattacharjee A (2023) Computational solar energy–ensemble learning methods for prediction of solar power generation based on meteorological parameters in Eastern India. Renew Energy Focus 44:277–294. (PMID: 10.1016/j.ref.2023.01.006) ; Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. Paper presented at the Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785–794. ; Coskuner G, Jassim MS, Zontul M, Karateke S (2021) Application of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastes. Waste Manage Res 39(3):499–507. (PMID: 10.1177/0734242X20935181) ; deHaan E, Moon JR, Shipman JE, Swanquist QT, Whited RL (2023) Control variables in interactive models. J Financ Report 8(2):77–85. (PMID: 10.2308/JFR-2021-023) ; Devi A, Ravindra K, Kaur M, Kumar R (2019) Evaluation of biomedical waste management practices in public and private sector of health care facilities in India. Environ Sci Pollut Res 26:26082–26089. (PMID: 10.1007/s11356-019-05785-9) ; EPA. (2022). Medical waste. Retrieved from https://www.epa.gov/rcra/medical-waste. ; Erdebilli B, Devrim-İçtenbaş B (2022) Ensemble voting regression based on machine learning for predicting medical waste: a case from Turkey. Mathematics 10(14):2466. (PMID: 10.3390/math10142466) ; Géron A (2022) Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (Third ed.). O’Reilly Media, USA. ; Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63:3–42. (PMID: 10.1007/s10994-006-6226-1) ; Gheyas IA, Smith LS (2010) A neural network-based framework for the reconstruction of incomplete data sets. Neurocomputing 73(16–18):3039–3065. (PMID: 10.1016/j.neucom.2010.06.021) ; Golbaz S, Nabizadeh R, Sajadi HS (2019) Comparative study of predicting hospital solid waste generation using multiple linear regression and artificial intelligence. J Environ Health Sci Eng 17(1):41–51. (PMID: 10.1007/s40201-018-00324-z) ; Gutierrez, G. (2020). Artificial intelligence in the intensive care unit. Annual Update in Intensive Care and Emergency Medicine 2020, 667-681. ; Hao H, Zhang J, Zhang Q, Yao L, Sun Y (2021) Improved gray neural network model for healthcare waste recycling forecasting. J Comb Optim 42:813–830. (PMID: 10.1007/s10878-019-00482-2) ; Heddam S (2021) Intelligent data analytics approaches for predicting dissolved oxygen concentration in river: extremely randomized tree versus random forest, MLPNN and MLR (T. G, Sitharam. Springer Singapore, Singapore. ; Information & eGovernment Authority. (2022). Bahrain open data portal. Retrieved from https://www.data.gov.bh/pages/homepage/. ; Jahandideh S, Jahandideh S, Asadabadi EB, Askarian M, Movahedi MM, Hosseini S, Jahandideh M (2009) The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation. Waste Manage 29(11):2874–2879. (PMID: 10.1016/j.wasman.2009.06.027) ; Jassim MS, Coskuner G, Zontul M (2022) Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation. Waste Manage Res 40(2):195–204. (PMID: 10.1177/0734242X211008526) ; Jassim MS, Coskuner G, Sultana N, Hossain SZ (2023) Forecasting domestic waste generation during successive COVID-19 lockdowns by bidirectional LSTM super learner neural network. Appl Soft Comput 133:109908. (PMID: 10.1016/j.asoc.2022.109908) ; Karpušenkaitė A, Ruzgas T, Denafas G (2016) Forecasting medical waste generation using short and extra short datasets: case study of Lithuania. Waste Manage Res 34(4):378–387. (PMID: 10.1177/0734242X16628977) ; Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, . . . Liu T-Y (2017) Lightgbm: a highly efficient gradient boosting decision tree. Paper presented at the Advances in Neural Information Processing Systems, Long Beach, p 30. ; Kilimci ZH (2022) Ensemble regression-based gold price (XAU/USD) prediction. J Emerg Com Tech 2(1):7–12. ; Kokol P, Kokol M, Zagoranski S (2022) Machine learning on small size samples: a synthetic knowledge synthesis. Sci Prog 105(1):1–16. (PMID: 10.1177/00368504211029777) ; Komilis D, Fouki A, Papadopoulos D (2012) Hazardous medical waste generation rates of different categories of health-care facilities. Waste Manage 32(7):1434–1441. (PMID: 10.1016/j.wasman.2012.02.015) ; Korkut EN (2018) Estimations and analysis of medical waste amounts in the city of Istanbul and proposing a new approach for the estimation of future medical waste amounts. Waste Manage 81:168–176. (PMID: 10.1016/j.wasman.2018.10.004) ; Kwak SK, Kim JH (2017) Statistical data preparation: management of missing values and outliers. Korean J Anesthesiol 70(4):407–411. (PMID: 10.4097/kjae.2017.70.4.407) ; Lai, C.-H., Yang, C.-T., Kristiani, E., Liu, J.-C., & Chan, Y.-W. (2020). Using xgboost for cyberattack detection and analysis in a network log system with elk stack. Paper presented at the Frontier Computing: Theory, Technologies and Applications (FC 2019) 8, 302–311, Singapore. ; Li Y, Chen W (2020) A comparative performance assessment of ensemble learning for credit scoring. Mathematics 8(10):1756. (PMID: 10.3390/math8101756) ; Li S, Zhang X (2020) Research on orthopedic auxiliary classification and prediction model based on XGBoost algorithm. Neural Comput Appl 32:1971–1979. (PMID: 10.1007/s00521-019-04378-4) ; Lindahl JF, Grace D (2015) The consequences of human actions on risks for infectious diseases: a review. Infect Ecol Epidemiol 5(1):30048. ; Mahesh B (2020) Machine learning algorithms-a review. Int J Sci Res 9:381–386. ; Matsuto T, Tanaka N (1993) Data analysis of daily collection tonnage of residential solid waste in Japan. Waste Manage Res 11(4):333–343. (PMID: 10.1177/0734242X9301100407) ; Medium. (2019). Stats series #1 — elastic net. Retrieved from https://medium.com/@konark145/stats-series-1-elastic-net-655786810be7. ; Microsoft. (2023a). Explore automated machine learning in Azure ML. Retrieved from https://microsoftlearning.github.io/AI-900-AIFundamentals/instructions/02-module-02.html. ; Microsoft. (2023b). LightGBM. Retrieved from https://github.com/microsoft/LightGBM. ; MOH. (2023). Salmaniya Medical Complex. Retrieved from https://www.moh.gov.bh/HealthInstitution/SalmaniyaMedicalComplex?lang=en. ; Mohamed L, Ebrahim S, Al-Thukair A (2009) Hazardous healthcare waste management in the Kingdom of Bahrain. Waste Manage 29(8):2404–2409. (PMID: 10.1016/j.wasman.2009.02.015) ; Murphy KP (2012) Machine learning: a probabilistic perspective. MIT Press, London, England. ; Nattee C, Khamsemanan N, Lawtrakul L, Toochinda P, Hannongbua S (2017) A novel prediction approach for antimalarial activities of Trimethoprim, Pyrimethamine, and Cycloguanil analogues using extremely randomized trees. J Mol Graph Model 71:13–27. (PMID: 10.1016/j.jmgm.2016.09.010) ; Neves AC, Maia CC, de Castro e Silva ME, Vimieiro GV, Gomes Mol MP (2022) Analysis of healthcare waste management in hospitals of Belo Horizonte Brazil. Environ Sci Pollut Res 29(60):90601–90614. (PMID: 10.1007/s11356-022-22113-w) ; Nguyen, X. C., Nguyen, T. T. H., La, D. D., Kumar, G., Rene, E. R., Nguyen, D. D., . . . Nguyen, V. K. (2021). Development of machine learning-based models to forecast solid waste generation in residential areas: a case study from Vietnam. Resour Conserv Recycl 167:105381. ; NHRA. (2020). Annual report 2020. Retrieved from https://www.nhra.bh/About/AnnualReport/MediaHandler/ImageHandler/documents/About/Annual%20Report/NHRA%20Annual%20Report%202020n.pdf. ; Noori R, Abdoli M, Ghasrodashti AA, Jalili Ghazizade M (2009) Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: a case study of Mashhad. Environ Prog Sustain Energ 28(2):249–258. (PMID: 10.1002/ep.10317) ; Park J, Moon J, Jung S, Hwang E (2020) Multistep-ahead solar radiation forecasting scheme based on the light gradient boosting machine: a case study of Jeju Island. Remote Sens 12(14):2271. (PMID: 10.3390/rs12142271) ; Portal, KOBSN. (2022). Public health sector. Retrieved from https://www.bahrain.bh/new/en/health-public_en.html. ; Ramírez-Gallego S, Krawczyk B, García S, Woźniak M, Herrera F (2017) A survey on data preprocessing for data stream mining: current status and future directions. Neurocomputing 239:39–57. (PMID: 10.1016/j.neucom.2017.01.078) ; Rashid, H. M. M., Yatim, S. R. M., Abdullah, S., Pejabat, M. Y. A. F., Nazim, I. A., & Ahmad, Z. (2022). Forecasting medical waste generation in Hospital Taiping, Perak using nonlinear autoregressive (NAR) neural network method. Paper presented at the 2022 3rd International Conference on Artificial Intelligence and Data Sciences (AiDAS), September 7, 243–247, IPOH, Malaysia. ; Sabour MR, Mohamedifard A, Kamalan H (2007) A mathematical model to predict the composition and generation of hospital wastes in Iran. Waste Manage 27(4):584–587. https://doi.org/10.1016/j.wasman.2006.05.010. (PMID: 10.1016/j.wasman.2006.05.010) ; Scribbr. (2021). Control variables | What are they & why do they matter? Retrieved from https://www.scribbr.com/methodology/control-variable/. ; Sengupta D, Agrahari S (2017) Modelling trends in solid and hazardous waste management, 1st edn. Springer, Singapore. (PMID: 10.1007/978-981-10-2410-8) ; Shahhosseini M, Hu G, Pham H (2022) Optimizing ensemble weights and hyperparameters of machine learning models for regression problems. Machine Learning with Applications 7:100251. (PMID: 10.1016/j.mlwa.2022.100251) ; Tesfahun E, Kumie A, Beyene A (2016) Developing models for the prediction of hospital healthcare waste generation rate. Waste Manage Res 34(1):75–80. https://doi.org/10.1177/0734242X15607422. (PMID: 10.1177/0734242X15607422) ; Thakur V, Ramesh A (2018) Analyzing composition and generation rates of biomedical waste in selected hospitals of Uttarakhand, India. J Mater Cycles Waste Manage 20:877–890. (PMID: 10.1007/s10163-017-0648-7) ; The Word Bank. (2023). DataBank-WDI database archives. Retrieved from https://databank.worldbank.org/source/wdi-database-archives. ; Tofallis C (2015) A better measure of relative prediction accuracy for model selection and model estimation. J Oper Res Soc 66:1352–1362. https://doi.org/10.1057/jors.2014.103. (PMID: 10.1057/jors.2014.103) ; Vinayak, R. K., & Gilad-Bachrach, R. (2015). Dart: dropouts meet multiple additive regression trees. Paper presented at the Artificial Intelligence and Statistics, February 21, 489–497, San Diego, CA, USA. ; Wang W, Liang J, Liu R, Song Y, Zhang M (2022) A robust variable selection method for sparse online regression via the elastic net penalty. Mathematics 10(16):2985. https://doi.org/10.3390/math10162985. (PMID: 10.3390/math10162985) ; Wei Y, Cui M, Ye Z, Guo Q (2021) Environmental challenges from the increasing medical waste since SARS outbreak. J Clean Prod 291:125246. https://doi.org/10.1016/j.jclepro.2020.125246. (PMID: 10.1016/j.jclepro.2020.125246) ; WHO. (2014). Safe management of wastes from health-care activities: World Health Organization. ; Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol 67(2):301–320. (PMID: 10.1111/j.1467-9868.2005.00503.x)
- Contributed Indexing: Keywords: Associated factors; Ensemble voting regression; Kingdom of Bahrain; Machine learning; Medical waste prediction; Sustainability
- Substance Nomenclature: 0 (Medical Waste)
- Entry Date(s): Date Created: 20240527 Date Completed: 20240618 Latest Revision: 20240626
- Update Code: 20240627
|
