1. Liu Y, Wang Z, Ren J, Tian Y, Zhou M, Zhou T, et al. A COVID-19 Risk Assessment Decision Support System for General Practitioners: Design and Development Study. Journal of medical Internet research. 2020;22(6):e19786.
2. Alom MZ, Rahman M, Nasrin MS, Taha TM, Asari VK. COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches. arXiv preprint arXiv:200403747. 2020.
3. Bansal A, Padappayil RP, Garg C, Singal A, Gupta M, Klein A. Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review. Journal of Medical Systems. 2020;44(9).
4. Lai C-C, Shih T-P, Ko W-C, Tang H-J, Hsueh P-R. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. International Journal of Antimicrobial Agents. 2020;55(3):105924.
5. Hussain A, Bhowmik B, do Vale Moreira NC. COVID-19 and diabetes: Knowledge in progress. Diabetes Research and Clinical Practice. 2020;162.
6. Moujaess E, Kourie HR, Ghosn M. Cancer patients and research during COVID-19 pandemic: A systematic review of current evidence. Critical Reviews in Oncology/Hematology. 2020;150:102972.
7. Yadaw AS, Li Y-c, Bose S, Iyengar R, Bunyavanich S, Pandey G. Clinical features of COVID-19 mortality: development and validation of a clinical prediction model. The Lancet Digital Health. 2020;2(10):e516-e25.
8. Hong Y, Wu X, Qu J, Gao Y, Chen H, Zhang Z. Clinical characteristics of coronavirus disease 2019 and development of a prediction model for prolonged hospital length of stay. Annals of translational medicine. 2020;8(7).
9. Daghistani TA, Elshawi R, Sakr S, Ahmed AM, Al-Thwayee A, Al-Mallah MH. Predictors of in-hospital length of stay among cardiac patients: A machine learning approach. International journal of cardiology. 2019;288:140-7.
10. Dan T, Li Y, Zhu Z, Chen X, Quan W, Hu Y, et al., editors. Machine Learning to Predict ICU Admission, ICU Mortality and Survivors’ Length of Stay among COVID-19 Patients: Toward Optimal Allocation of ICU Resources. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2020: IEEE.
11. Wu C, Glass S, Demars S, Tulloch-Palomino LG, Wander PL. Estimated excess acute-care length of stay and extra cost of testing-based versus symptom-based isolation strategies among veterans hospitalized with coronavirus disease 2019 (COVID-19) discharging to a congregate setting. Infection control and hospital epidemiology. 2020:1-3.
12. Ayyoubzadeh SM, Ghazisaeedi M, Kalhori SRN, Hassaniazad M, Baniasadi T, Maghooli K, et al. A study of factors related to patients’ length of stay using data mining techniques in a general hospital in southern Iran. Health information science and systems. 2020;8(1):1-11.
13. Jang SY, Seon J-Y, Yoon S-J, Park S-Y, Lee SH, Oh I-H. Comorbidities and Factors Determining Medical Expenses and Length of Stay for Admitted COVID-19 Patients in Korea. Risk Management and Healthcare Policy. 2021;14.
14. Thiruvengadam G, Lakshmi M, Ramanujam R. A Study of Factors Affecting the Length of Hospital Stay of COVID-19 Patients by Cox-Proportional Hazard Model in a South Indian Tertiary Care Hospital. Journal of Primary Care & Community Health. 2021;12:21501327211000231.
15. Wu S, Xue L, Legido-Quigley H, Khan M, Wu H, Peng X, et al. Understanding factors influencing the length of hospital stay among non-severe COVID-19 patients: A retrospective cohort study in a Fangcang shelter hospital. PloS one. 2020;15(10):e0240959.
16. Alwafi H, Naser AY, Qanash S, Brinji AS, Ghazawi MA, Alotaibi B, et al. Predictors of Length of Hospital Stay, Mortality, and Outcomes Among Hospitalised COVID-19 Patients in Saudi Arabia: A Cross-Sectional Study. Journal of Multidisciplinary Healthcare. 2021;14:839.
17. Calman YB-L, Gelbshtein U, Liverant-Taub S, Ziv A, Eytan D, Gorfine M, et al. Development and validation of a machine learning model for predicting illness trajectory and hospital resource utilization of COVID-19 hospitalized patients–a nationwide study. 2020.
18. Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Comparison of Four Data Mining Algorithms for Predicting Colorectal Cancer Risk. Journal of Advances in Medical and Biomedical Research.29(133):100-8.
19. Nassif AB, Azzeh M, Banitaan S, Neagu D. Guest editorial: special issue on predictive analytics using machine learning. Springer; 2016.
20. Hernandez-Suarez DF, Ranka S, Kim Y, Latib A, Wiley J, Lopez-Candales A, et al. Machine-learning-based in-hospital mortality prediction for transcatheter mitral valve repair in the United States. Cardiovascular Revascularization Medicine. 2020.
21. Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Comparison of Four Data Mining Algorithms for Predicting Colorectal Cancer Risk. Journal of Advances in Medical and Biomedical Research. 2021;29(133):100-8.
22. Streun GL, Elmiger MP, Dobay A, Ebert L, Kraemer T. A machine learning approach for handling big data produced by high resolution mass spectrometry after data independent acquisition of small molecules - Proof of concept study using an artificial neural network for sample classification. Drug testing and analysis. 2020;12(6):836-45.
23. Yang H, Zhang Z, Zhang J, Zeng XC. Machine learning and artificial neural network prediction of interfacial thermal resistance between graphene and hexagonal boron nitride. Nanoscale. 2018;10(40):19092-9.
24. Hijry H, Olawoyin R, editors. Application of Machine Learning Algorithms for Patient Length of Stay Prediction in Emergency Department During Hajj. 2020 IEEE International Conference on Prognostics and Health Management (ICPHM); 2020: IEEE.
25. Kabir S, Farrokhvar L, editors. Non-Linear Feature Selection for Prediction of Hospital Length of Stay. 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA); 2019: IEEE.
26. Kulkarni H, Thangam M, Amin AP. Artificial neural network‐based prediction of prolonged length of stay and need for post‐acute care in acute coronary syndrome patients undergoing percutaneous coronary intervention. European Journal of Clinical Investigation. 2021;51(3):e13406.
27. Morton A, Marzban E, Giannoulis G, Patel A, Aparasu R, Kakadiaris IA, editors. A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients. 2014 13th International Conference on Machine Learning and Applications; 2014: IEEE.
28. Neto C, Brito M, Peixoto H, Lopes V, Abelha A, Machado J, editors. Prediction of length of stay for stroke patients using artificial neural networks. World Conference on Information Systems and Technologies; 2020: Springer.
29. Karegowda AG, Manjunath A, Jayaram M. Comparative study of attribute selection using gain ratio and correlation based feature selection. International Journal of Information Technology and Knowledge Management. 2010;2(2):271-7.
30. Fazlollahi P, Afarineshkhaki A, Nikbakhsh R. Predicting the Medals of the Countries Participating in the Tokyo 2020 Olympic Games Using the Test of Networks of Multilayer Perceptron (MLP). Annals of Applied Sport Science. 2020;8(4):1-12.
31. Theerthagiri P, Gopala Krishnan C, Nishan AH. Prognostic analysis of hyponatremia for diseased patients using multilayer perceptron classification technique. EAI Endorsed Transactions on Pervasive Health and Technology. 2021;7(26).
32. Shah Z, Raja MAZ, Chu YM, Khan WA, Abbas SZ, Shoaib M, et al. Computational intelligence of Levenberg-Marquardt backpropagation neural networks to study the dynamics of expanding/contracting cylinder for Cross magneto-nanofluid flow model. Physica Scripta. 2021;96(5).
33. Moshkbar-Bakhshayesh K. Bayesian regularization of multilayer perceptron neural network for estimation of mass attenuation coefficient of gamma radiation in comparison with different supervised model-free methods. Journal of Instrumentation. 2020;15(11).
34. Ghani NAM, Kamaruddin SA, Ramli NM, Musirin I, Hashim H. Enhanced BFGS quasi-newton backpropagation models on MCCI data. Indonesian Journal of Electrical Engineering and Computer Science. 2017;8(1):101-6.
35. Saputra W, Tulus T, Zarlis M, Sembiring RW, Hartama D, editors. Analysis Resilient Algorithm on Artificial Neural Network Backpropagation. Journal of Physics: Conference Series; 2017.
36. Aburaed N, Atalla S, Mukhtar H, Al-Saad M, Mansoor W, editors. Scaled conjugate gradient neural network for optimizing indoor positioning system. 2019 International Symposium on Networks, Computers and Communications, ISNCC 2019; 2019.
37. Dawahdeh M, Sulaiman IM, Rivaie M, Mamat M. A new spectral conjugate gradient method with strong wolfe-powell line search. International Journal of Emerging Trends in Engineering Research. 2020;8(2):391-7.
38. Abid S, Mouelhi A, Fnaiech F, editors. Accelerating the multilayer perceptron learning with the Davidon Fletcher powell algorithm. IEEE International Conference on Neural Networks - Conference Proceedings; 2006.
39. Latif M, Herawati S, editors. The application of eemd and neural network based on polak-ribiére conjugate gradient algorithm for crude oil prices forecasting. MATEC web of conferences; 2016: EDP Sciences.
40. Solikhun, Wahyudi M, Safii M, Zarlis M, editors. Backpropagation Network Optimization Using One Step Secant (OSS) Algorithm. IOP Conference Series: Materials Science and Engineering; 2020.
41. Lu Y, Li W, Wang H. A Batch Variable Learning Rate Gradient Descent Algorithm with the Smoothing L1/2 Regularization for Takagi-Sugeno Models. IEEE Access. 2020;8:100185-93.
42. Xue H, Shao Z, Sun H. Data classification based on fractional order gradient descent with momentum for RBF neural network. Network: Computation in Neural Systems. 2020.
43. Lapidus N, Zhou X, Carrat F, Riou B, Zhao Y, Hejblum G. Biased and unbiased estimation of the average length of stay in intensive care units in the Covid-19 pandemic. Annals of intensive care. 2020;10(1):135.
44. Bacchi S, Tan Y, Oakden-Rayner L, Jannes J, Kleinig T, Koblar S. Machine Learning in the Prediction of Medical Inpatient Length of Stay. Internal medicine journal. 2020.
45. Symum H, Zayas-Castro JL. Prediction of Chronic Disease-Related Inpatient Prolonged Length of Stay Using Machine Learning Algorithms. Healthc Inform Res. 2020;26(1):20-33.
46. . !!! INVALID CITATION !!! (3-5).
47. Amellas Y, Djebli A, Echchelh A. Levenberg-marquardt training function using on mlp, rnn and elman neural network to optimize hourly forecasting in tetouan city (Northern Morocco). Journal of Engineering Science and Technology Review. 2020;13(1):67-71.
48. Miaoli M, Xiaolong W, Honggui H, editors. Accelerated Levenberg-Marquardt Algorithm for Radial Basis Function Neural Network. Proceedings - 2020 Chinese Automation Congress, CAC 2020; 2020.
49. ColaÇo MJ, Orlande HRB. Comparison of different versions of the conjugate gradient method of function estimation. Numerical Heat Transfer; Part A: Applications. 1999;36(2):229-49.
50. Jeong SB, Lee SJ, Park GJ. Improvement of the convergence capability of a single loop single vector approach using conjugate gradient for a concave function. Transactions of the Korean Society of Mechanical Engineers, A. 2012;36(7):805-11.
51. Dutta M, Chatterjee A, Rakshit A, editors. A resilient backpropagation neural network based phase correction system for automatic digital AC bridges. CPEM Digest (Conference on Precision Electromagnetic Measurements); 2004.
52. Wang X, Wang H, Dai G, Tang Z. A reliable resilient backpropagation method with gradient ascent. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)2006. p. 236-44.
53. Sotirov S, Atanassov K, Krawczak M. Generalized net model for parallel optimization of feed-forward neural network with variable learning rate backpropagation algorithm with time limit. Studies in Computational Intelligence2010. p. 361-71.
54. Yu F, Hu Z, editors. Variable weighted learning algorithm and its convergence rate. 5th International Conference on Natural Computation, ICNC 2009; 2009.
55. Khan I, Raja MAZ, Shoaib M, Kumam P, Alrabaiah H, Shah Z, et al. Design of Neural Network with Levenberg-Marquardt and Bayesian Regularization Backpropagation for Solving Pantograph Delay Differential Equations. IEEE Access. 2020;8:137918-33.
56. Priya A, Garg S. A Comparison of Prediction Capabilities of Bayesian Regularization and Levenberg–Marquardt Training Algorithms for Cryptocurrencies. Smart Innovation, Systems and Technologies2020. p. 657-64.
57. Maharlou H, Kalhori SRN, Shahbazi S, Ravangard R. Predicting length of stay in intensive care units after cardiac surgery: comparison of artificial neural networks and adaptive neuro-fuzzy system. Healthcare informatics research. 2018;24(2):109.
58. Launay C, Rivière H, Kabeshova A, Beauchet O. Predicting prolonged length of hospital stay in older emergency department users: use of a novel analysis method, the Artificial Neural Network. European journal of internal medicine. 2015;26(7):478-82.
59. Bacchi S, Gluck S, Tan Y, Chim I, Cheng J, Gilbert T, et al. Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study. Internal and emergency medicine. 2020;15(6):989-95.
60. East A, Ray S, Pope R, Cortina-Borja M, Sebire NJ. 45 Predicting long length of stay in a paediatric intensive care unit using machine learning. BMJ Publishing Group Ltd; 2020.
61. ÇETİN Ş, ULGEN A, ŞIVGIN H, Wentian L. A Study on Factors Impacting Length of Hospital Stay of COVID-19 Inpatients. Journal of Contemporary Medicine.11(3 (Forthcoming Issue-Gelecek Sayı)):396-404.
62. Guo A, Lu J, Tan H, Kuang Z, Luo Y, Yang T, et al. Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study. Scientific Reports. 2021;11(1):1-7.
63. Afshar S, Warden E, Manochehri H, Saidijam M. Application of artificial neural network in miRNA biomarker selection and precise diagnosis of colorectal cancer. Iranian biomedical journal. 2019;23(3):175-83.