Comparing traditional modeling approaches versus predictive analytics methods for predicting multiple sclerosis relapse

Bozkaya, 2017, The cost-effectiveness of disease-modifying therapies for the treatment of relapsing-remitting multiple sclerosis, J. Med. Econ., 20, 297, 10.1080/13696998.2016.1258366 Chawla, 2002, Smote: synthetic minority over-sampling technique, J. Artif. Intell. Res., 16, 321, 10.1613/jair.953 Chicco, 2020, The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation, BMC Genomics, 21, 10.1186/s12864-019-6413-7 Couronne, 2018, Random forest versus logistic regression: a large-scale benchmark experiment, BMC Bioinformatics, 19, 10.1186/s12859-018-2264-5 2021 Held, 2005, Predictors of relapse rate in MS clinical trials, Neurology, 65, 1769, 10.1212/01.wnl.0000187122.71735.1f Hastie, 2017 Iezzoni, 2007, Health, disability, and life insurance experiences of working-age persons with multiple sclerosis, Mult. Scler., 13, 534, 10.1177/1352458506071356 James, 2017 Jones, 2016, Quantifying the relationship between increased disability and health care resource utilization, quality of life, work productivity, health care costs in patients with multiple sclerosis in the US, BMC Health Serv. Res., 16, 10.1186/s12913-016-1532-1 O’Connell, 2014, Economic costs associated with an MS relapse, Mult. Scler. Relat. Disord., 3, 678, 10.1016/j.msard.2014.09.002 Oliver, 2021, System-Level Variation in Multiple Sclerosis Care Outcomes: initial Findings from the Multiple Sclerosis Continuous Quality Improvement Research Collaborative, Popul. Health Manag. Polit, 2009, International differences in nursing research, 2005-2006, J. Nurs. Scholarsh., 41, 44, 10.1111/j.1547-5069.2009.01250.x Rothwell, 2005, External validity of randomised controlled trials: “to whom do the results of this trial apply?”, The Lancet, 365, 82, 10.1016/S0140-6736(04)17670-8 Sormani, 2007, A composite score to predict short-term disease activity in patients with relapsing-remitting MS, Neurology, 69, 1230, 10.1212/01.wnl.0000276940.90309.15 Tabachnick, 2021 Velez de Mendizabal, 2013, Predicting relapsing-remitting dynamics in multiple sclerosis using discrete distribution models: a population approach, PLoS ONE, 8, e73361, 10.1371/journal.pone.0073361 Vollmer, 2007, The natural history of relapses in multiple sclerosis, J. Neurol. Sci., 256 Wallin, 2019, The prevalence of MS in the United States: a population-based estimate using health claims data, Neurology, 92, e1029, 10.1212/WNL.0000000000007035 Zhang, 2019, A comparison of logistic regression, random forest models in predicting the risk of diabetes