Page 58 - AIH-1-3
P. 58

Artificial Intelligence in Health                                  Predicting mortality in COVID-19 using ML



               doi: 10.2307/1403797                               doi: 10.1016/j.imu.2022.101023
            36.  Fitton D.  Evaluating Models in Azure Machine Learning   46.  Al-Shaikh A, Mahafzah BA, Alshraideh M. Hybrid harmony
               (Part  1: Classification). Adatis; 2020. Available from:   search algorithm for social network contact tracing of
               https://adatis.co.uk/evaluating-models-in-azure-   COVID-19. Soft Comput. 2023;27(6):3343-3365.
               machine-learning-part-1-classification [Last accessed on
               2023 Dec 18].                                      doi: 10.1007/s00500-021-05948-2
            37.  Classification: ROC Curve and AUC. Machine Learning.   47.  Mandala SK. Unveiling the unborn: Advancing fetal health
               Google for Developers. Google Machine Learning Education;   classification through machine learning. Artif Intell Health.
               2022. Available from: https://developers.google.com/  2023;1(1):2121.
               machine-learning/crash-course/classification/roc-and-auc      doi: 10.36922/aih.2121
               [Last accessed on 2023 Dec 18]
                                                               48.  Al-Tawil M, Mahafzah BA, Al Tawil A, Aljarah I. Bio-
            38.  Josephus BO, Nawir AH, Wijaya E, Moniaga JV, Ohyver M.   inspired machine learning approach to type  2 diabetes
               Predict mortality in patients infected with COVID-19 virus   detection. Symmetry (Basel). 2023;15(3):764.
               based on observed characteristics of the patient using
               logistic regression. Procedia Comput Sci. 2021;179:871-877.     doi: 10.3390/sym15030764
               doi: 10.1016/j.procs.2021.01.076                49.  Umar BU, Ajao LA, Dogo EM, Ajao FJ, Atama M. Artificial
                                                                  intelligence model for prediction of cardiovascular disease:
            39.  Yan L, Zhang HT, Goncalves J,  et al. An interpretable
               mortality prediction model for COVID-19  patients.  Nat   An empirical study. Artif Intell Health. 2023;1(1):1746.
               Mach Intell. 2020;2(5):283-288.                    doi: 10.36922/aih.1746
               doi: 10.1038/s42256-020-0180-7                  50.  Chawla NV, Bowyer KW, Hall LO, Philip Kegelmeyer W.
            40.  Pourhomayoun M, Shakibi M. Predicting mortality risk in   SMOTE: Synthetic minority over-sampling technique.
               patients with COVID-19 using machine learning to help   J Artif Intell Res. 2002;30(2):321-357.
               medical decision-making. Smart Health. 2021;20:100178.  51.  Rosenblatt F. The perceptron: A  probabilistic model for
               doi: 10.1016/j.smhl.2020.100178                    information storage and organization in the brain. Psychol
                                                                  Rev. 1958;65(6):386-408.
            41.  Naseem  M,  Arshad  H,  Hashmi  SA,  Irfan  F,  Ahmed  FS.
               Predicting mortality in SARS-COV-2 (COVID-19) positive      doi: 10.1037/h0042519
               patients in the inpatient setting using a novel deep neural   52.  Abuqaddom I, Mahafzah BA, Faris H. Oriented stochastic
               network. Int J Med Inform. 2021;154:104556.
                                                                  loss descent algorithm to train very deep multi-layer neural
               doi: 10.1016/j.ijmedinf.2021.104556                networks without vanishing gradients.  Knowl Based Syst.
                                                                  2021;230:107391.
            42.  Chadaga K, Prabhu S, Umakanth S,  et al. COVID-19
               mortality prediction among patients using epidemiological      doi: 10.1016/j.knosys.2021.107391
               parameters: An ensemble machine learning approach. Eng
               Sci. 2021;16:221-33.                            53.  Neural Network Models (supervised); 2021. Available from:
                                                                  https://scikit-learn.org/stable/modules/neural_networks_
               doi: 10.30919/es8d579                              supervised.html [Last accessed on 2023 Dec 18].
            43.  Franklin MR. Mexico COVID-19 Clinical Data; 2019. Available   54.  Cover TM, Hart PE. Nearest neighbor pattern classification.
               from:  https://www.kaggle.com/datasets/marianarfranklin/  IEEE Trans Inf Theory. 1967;13(1):21-27.
               mexico-covid19-clinical-data [Last accessed on 2023 Dec 18].
                                                                  doi: 10.1109/TIT.1967.1053964
            44.  Rai N, Kaushik N, Kumar D, Raj C, Ali A. Mortality
               prediction of COVID-19 patients using soft voting classifier.   55.  Kubat  M.  An Introduction to Machine Learning.  Berlin:
               Int J Cogn Comput Eng. 2022;3:172-179.             Springer; 2017.

               doi: 10.1016/j.ijcce.2022.09.001                   doi: 10.1007/978-3-319-63913-0
            45.  Bárcenas R, Fuentes-García R. Risk assessment in COVID-  56.  Glossary of Common Terms and API; 2007. Available from:
               19 patients: A multiclass classification approach. Inform Med   https://scikit-learn.org/stable/glossary.html#term-feature_
               Unlocked. 2022;32:101023.                          importances [Last accessed on 2023 Dec 18].










            Volume 1 Issue 3 (2024)                         52                               doi: 10.36922/aih.2591
   53   54   55   56   57   58   59   60   61   62   63