Predicting Diabetic Patient Hospital Readmission Using Optimized Random Forest and Firefly Evolutionary Algorithm
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“Diabetes-WHO.” https://www.who.int/health-topics/diabetes#tab= tab_1 (accessed Dec. 11, 2020).
S. Bolourani et al., “Using machine learning to predict early readmission following esophagectomy,” J. Thorac. Cardiovasc. Surg., 2020, doi: 10.1016/j.jtcvs.2020.04.172.
P. Wolff, M. Graña, A. R. Sebastií¡n, and M. B. Yarza, “Machine Learning Readmission Risk Modeling : A Pediatric Case Study,” vol. 2019, 2019.
Y. Tseng, H. Wang, T. Lin, J. Lu, C. Hsieh, and C. Liao, “Development of a Machine Learning Model for Survival Risk Stratification of Patients With Advanced Oral Cancer,” vol. 3, no. 8, 2020, doi: 10.1001/jamanetworkopen.2020.11768.
A. Choudhury and D. C. M. Greene, “Evaluating Patient Readmission Risk: A Predictive Analytics Approach,” Am. J. Eng. Appl. Sci., vol. 11, no. 4, pp. 1320-1331, 2018, doi: 10.3844/ajeassp.2018.1320.1331.
F. Alshakhs, H. Alharthi, N. Aslam, I. U. Khan, and M. Elasheri, “Predicting postoperative length of stay for isolated coronary artery bypass graft patients using machine learning,” Int. J. Gen. Med., vol. 13, 2020, doi: 10.2147/IJGM.S250334.
M. S. Bhuvan, A. Kumar, A. Zafar, and V. Kishore, “Identifying Diabetic Patients with High Risk of Readmission.” arXiv preprint arXiv:1602.04257.
R. Duggal, S. Shukla, S. Chandra, and B. Shukla, “Impact of selected pre-processing techniques on prediction of risk of early readmission for diabetic patients in India,” Int. J. Diabetes Dev. Ctries., vol. 36, no. December, pp. 469-476, 2016, doi: 10.1007/s13410-016-0495-4.
R. Duggal, S. Shukla, S. Chandra, B. Shukla, and S. K. Khatri, “Predictive risk modelling for early hospital readmission of patients with diabetes in India,” Int. J. Diabetes Dev. Ctries., vol. 36, no. 4, pp. 519-528, 2016, doi: 10.1007/s13410-016-0511-8.
S. Cui, D. Wang, Y. Wang, P. W. Yu, and Y. Jin, “An improved support vector machine-based diabetic readmission prediction,” Comput. Methods Programs Biomed., vol. 166, pp. 123-135, 2018, doi: 10.1016/j.cmpb.2018.10.012.
Ghazo, Esraa. Prediction of Diabetic Patient Readmission Using Hybrid Ensemble Learning Diss. State University of New York at Binghamton, 2019.
M. Alloghani et al., “Implementation of machine learning algorithms to create diabetic patient re-admission profiles,” BMC Med. Inform. Decis. Mak., vol. 19, no. Suppl 9, pp. 1-16, 2019, doi: 10.1186/s12911-019-0990-x.
A. Hammoudeh, G. Al-Naymat, I. Ghannam, and N. Obied, “Predicting hospital readmission among diabetics using deep learning,” Procedia Comput. Sci., vol. 141, pp. 484-489, 2018, doi: 10.1016/j.procs.2018.10.138.
J. C. Ramírez and D. Herrera, “Prediction of Diabetic Patient Readmission Using Machine Learning,” Commun. Comput. Inf. Sci., vol. 1096 CCIS, pp. 78-88, 2019, doi: 10.1007/978-3-030-36211-9_7.
Sarthak, S. Shukla, and S. Prakash Tripathi, “Embpred30: Assessing 30-days readmission for diabetic patients using categorical embeddings,” Adv. Intell. Syst. Comput., vol. 1168, pp. 81-90, 2021, doi: 10.1007/978-981-15-5345-5_7.
C. I. Ossai and N. Wickramasinghe, “Intelligent therapeutic decision support for 30 days readmission of diabetic patients with different comorbidities,” J. Biomed. Inform., vol. 107, no. June, p. 103486, 2020, doi: 10.1016/j.jbi.2020.103486.
B. Strack et al., “Impact of HbA1c measurement on hospital readmission rates: Analysis of 70,000 clinical database patient records,” Biomed Res. Int., vol. 2014, 2014, doi: 10.1155/2014/781670.
C. Feng et al., “Log-transformation and its implications for data analysis,” Shanghai Arch. Psychiatry, vol. 26, no. 2, pp. 105-109, 2014, doi: 10.3969/j.issn.1002-0829.2014.02.
X. Yang, Nature-Inspired Metaheuristic Algorithms Second Edition, vol. 4, no. C. 2010.
Y. L. Pavlov, “Random forests,” Random For., pp. 1-122, 2019, doi: 10.1201/9780367816377-11.
W. P. K. Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, “Smote: synthetic minor- ity over-sampling technique,” J. Mach. Learn. Res., vol. 16, pp. 321-357, 2002.
T. Zhu, Y. Lin, and Y. Liu, “Synthetic minority oversampling technique for multiclass imbalance problems,” Pattern Recognit., vol. 72, pp. 327-340, 2017, doi: 10.1016/j.patcog.2017.07.024.
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