Comparison of Some Prediction Models and their Relevance in the Clinical Research
DOI:
https://doi.org/10.6000/1929-6029.2023.12.02Keywords:
Predictive modeling, Risk estimation, Probability, Public health, Clinical outcomesAbstract
In healthcare research, predictive modeling is commonly utilized to forecast risk variables and enhance treatment procedures for improved patient outcomes. Enormous quantities of data are being created as a result of recent advances in research, clinical trials, next-generation genomic sequencing, biomarkers, and transcriptional and translational studies. Understanding how to handle and comprehend scientific data to offer better treatment for patients is critical. Currently, multiple prediction models are being utilized to investigate patient outcomes. However, it is critical to recognize the limitations of these models in the research design and their unique benefits and drawbacks. In this overview, we will look at linear regression, logistic regression, decision trees, and artificial neural network prediction models, as well as their advantages and disadvantages. The two most perilous requirements for building any predictive healthcare model are feature selection and model validation. Typically, feature selection is done by a review of the literature and expert opinion on that subject. Model validation is also an essential component of every prediction model. It characteristically relates to the predictive model's performance and accuracy. It is strongly recommended that all clinical parameters should be thoroughly examined before using any prediction model.
References
van Leeuwen J. Approaches in Machine Learning, in Algorithms in Ambient Intelligence, Verhaegh WFJ, Aarts E, Korst J, Eds., Springer Netherlands: Dordrecht 2004; pp. 151-166. https://doi.org/10.1007/978-94-017-0703-9_8 DOI: https://doi.org/10.1007/978-94-017-0703-9_8
Aleskerov E, Freisleben B, Rao RB. CARDWATCH: a neural network based database mining system for credit card fraud detection. Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr), 1997; pp. 220-226.
Kim E, Kim W, Lee Y. Combination of multiple classifiers for the customer's purchase behavior prediction. Decision Support Systems 2003; 34(2): 167-175. https://doi.org/10.1016/S0167-9236(02)00079-9 DOI: https://doi.org/10.1016/S0167-9236(02)00079-9
Collins GS, et al. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Medicine 2011; 9(1): 103. https://doi.org/10.1186/1741-7015-9-103 DOI: https://doi.org/10.1186/1741-7015-9-103
Mallett S, et al. Reporting methods in studies developing prognostic models in cancer: a review. BMC Medicine 2010; 8(1): 20. https://doi.org/10.1186/1741-7015-8-20 DOI: https://doi.org/10.1186/1741-7015-8-20
Han K, Song K, Choi BW. How to Develop, Validate, and Compare Clinical Prediction Models Involving Radiological Parameters: Study Design and Statistical Methods. Korean J Radiol 2016; 17(3): 339-50. https://doi.org/10.3348/kjr.2016.17.3.339 DOI: https://doi.org/10.3348/kjr.2016.17.3.339
Collins GS, et al. External validation of multivariable pre-diction models: a systematic review of methodological con-duct and reporting. BMC Med Res Methodol 2014; 14: 40. https://doi.org/10.1186/1471-2288-14-40 DOI: https://doi.org/10.1186/1471-2288-14-40
Chidambaram AG, Josephson M. Clinical research study designs: The essentials. Pediatr Investig 2019; 3(4): 245-252. https://doi.org/10.1002/ped4.12166 DOI: https://doi.org/10.1002/ped4.12166
Riley RD, Ensor J, Snell KIE. Calculating the sample size required for developing a clinical prediction model 2020; 368: m441. https://doi.org/10.1136/bmj.m441 DOI: https://doi.org/10.1136/bmj.m441
Janssen KJ, et al. Dealing with missing predictor values when applying clinical prediction models. Clin Chem 2009; 55(5): 994-1001. https://doi.org/10.1373/clinchem.2008.115345 DOI: https://doi.org/10.1373/clinchem.2008.115345
Rath S, Tripathy A, Tripathy AR. Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model. Diabetes Metab Syndr 2020; 14(5): 1467-1474. https://doi.org/10.1016/j.dsx.2020.07.045 DOI: https://doi.org/10.1016/j.dsx.2020.07.045
Panda NR. A Review on Logistic Regression in Medical Research. National Journal of Community Medicine 2022; 13(04): 265-270. https://doi.org/10.55489/njcm.134202222 DOI: https://doi.org/10.55489/njcm.134202222
Panda NR, Pati JK, Bhuyan R. Role of Predictive Modeling in Healthcare Research: A Scoping Review. International Journal of Statistics in Medical Research 2022; 11: 77-81. https://doi.org/10.6000/1929-6029.2022.11.09 DOI: https://doi.org/10.6000/1929-6029.2022.11.09
Rajendra P, Latifi S. Prediction of diabetes using logistic regression and ensemble techniques. Computer Methods and Programs in Biomedicine Update 2021; 1: 100032. https://doi.org/10.1016/j.cmpbup.2021.100032 DOI: https://doi.org/10.1016/j.cmpbup.2021.100032
Liu L, et al. Dental Caries Prediction Based on a Survey of the Oral Health Epidemiology among the Geriatric Residents of Liaoning, China 2020; 2020: 5348730. https://doi.org/10.1155/2020/5348730 DOI: https://doi.org/10.1155/2020/5348730
Saroj RK, Anand M. Environmental factors prediction in preterm birth using comparison between logistic regression and decision tree methods: An exploratory analysis. Social Sciences & Humanities Open 2021; 4(1): 100216. https://doi.org/10.1016/j.ssaho.2021.100216 DOI: https://doi.org/10.1016/j.ssaho.2021.100216
Berhie KA, Gebresilassie HG. Logistic regression analysis on the determinants of stillbirth in Ethiopia. Maternal Health, Neonatology and Perinatology 2016; 2(1): 10. https://doi.org/10.1186/s40748-016-0038-5 DOI: https://doi.org/10.1186/s40748-016-0038-5
Aleem IS, Schemitsch EH, Hanson BP. What is a clinical decision analysis study? Indian J Orthop 2008; 42(2): 137-9. https://doi.org/10.4103/0019-5413.40248 DOI: https://doi.org/10.4103/0019-5413.40248
Brims FJ, et al. A Novel Clinical Prediction Model for Prognosis in Malignant Pleural Mesothelioma Using Decision Tree Analysis. J Thorac Oncol 2016; 11(4): 573-82. https://doi.org/10.1016/j.jtho.2015.12.108 DOI: https://doi.org/10.1016/j.jtho.2015.12.108
Shahid N, Rappon T, Berta W. Applications of artificial neural networks in health care organizational decision-making: A scoping review. PLOS ONE 2019; 14(2): e0212356. https://doi.org/10.1371/journal.pone.0212356 DOI: https://doi.org/10.1371/journal.pone.0212356
Patel JL, Goyal RK. Applications of artificial neural networks in medical science. Curr Clin Pharmacol 2007; 2(3): 217-26. https://doi.org/10.2174/157488407781668811 DOI: https://doi.org/10.2174/157488407781668811
Rau HH, et al. Development of a web-based liver cancer prediction model for type II diabetes patients by using an artificial neural network. Comput Methods Programs Biomed 2016; 125: 58-65. https://doi.org/10.1016/j.cmpb.2015.11.009 DOI: https://doi.org/10.1016/j.cmpb.2015.11.009
Li H, et al. An artificial neural network prediction model of congenital heart disease based on risk factors: A hospital-based case-control study. Medicine 2017; 96(6). https://doi.org/10.1097/MD.0000000000006090 DOI: https://doi.org/10.1097/MD.0000000000006090
Bekesiene S, Smaliukiene R, Vaicaitiene R. Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts. Mathematics 2021; 9(6): 626. https://doi.org/10.3390/math9060626 DOI: https://doi.org/10.3390/math9060626
Lee KS, Ahn KH. Artificial Neural Network Analysis of Spontaneous Preterm Labor and Birth and Its Major Determinants 2019; 34(16): e128. https://doi.org/10.3346/jkms.2019.34.e128 DOI: https://doi.org/10.3346/jkms.2019.34.e128
Xiao J, et al. Comparison and development of machine learning tools in the prediction of chronic kidney disease progression. Journal of Translational Medicine 2019; 17(1): 119. https://doi.org/10.1186/s12967-019-1860-0 DOI: https://doi.org/10.1186/s12967-019-1860-0
Lin SP, et al. A comparison of MICU survival prediction using the logistic regression model and artificial neural network model. J Nurs Res 2006; 14(4): 306-14. https://doi.org/10.1097/01.JNR.0000387590.19963.8e DOI: https://doi.org/10.1097/01.JNR.0000387590.19963.8e
Borzouei S, et al. Diagnosing thyroid disorders: Comparison of logistic regression and neural network models. J Family Med Prim Care 2020; 9(3): 1470-1476. https://doi.org/10.4103/jfmpc.jfmpc_910_19 DOI: https://doi.org/10.4103/jfmpc.jfmpc_910_19
Tong Z, et al. Development, Validation and Comparison of Artificial Neural Network Models and Logistic Regression Models Predicting Survival of Unresectable Pancreatic Cancer. Frontiers in Bioengineering and Biotechnology 2020; 8. https://doi.org/10.3389/fbioe.2020.00196 DOI: https://doi.org/10.3389/fbioe.2020.00196
Chaubey G, et al. Thyroid Disease Prediction Using Machine Learning Approaches. National Academy Science Letters 2021; 44(3): 233-238. https://doi.org/10.1007/s40009-020-00979-z DOI: https://doi.org/10.1007/s40009-020-00979-z
Owari Y, Miyatake N. Prediction of Chronic Lower Back Pain Using the Hierarchical Neural Network: Comparison with Logistic Regression-A Pilot Study. Medicina (Kaunas) 2019; 55(6). https://doi.org/10.3390/medicina55060259 DOI: https://doi.org/10.3390/medicina55060259
Li G, et al. Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province. PLOS Neglected Tropical Diseases 2018; 12(2): e0006262. https://doi.org/10.1371/journal.pntd.0006262 DOI: https://doi.org/10.1371/journal.pntd.0006262
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