Comparison of Some Prediction Models and their Relevance in the Clinical Research


  • Nihar Ranjan Panda Department of Medical Research, IMS and SUM Hospital, SOA deemed to be University, Bhubaneswar, Odisha, India and Department of Mathematics CV Raman Global University, Bhubaneswar, Odisha, India
  • Kamal Lochan Mahanta Department of Mathematics CV Raman Global University, Bhubaneswar, Odisha, India
  • Jitendra Kumar Pati Department of Mathematics Gandhi Engeeniearing College, Bhubaneswar, Odisha, India
  • Pavankumar Reddy Varanasi Department of Medical Research, IMS and SUM Hospital, SOA deemed to be University, Bhubaneswar, Odisha, India
  • Ruchi Bhuyan Department of Medical Research, IMS and SUM Hospital, SOA deemed to be University, Bhubaneswar, Odisha, India



Predictive modeling, Risk estimation, Probability, Public health, Clinical outcomes


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.


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How to Cite

Panda, N. R. ., Mahanta, K. L. ., Pati, J. K. ., Varanasi, P. R. ., & Bhuyan, R. . (2023). Comparison of Some Prediction Models and their Relevance in the Clinical Research. International Journal of Statistics in Medical Research, 12, 12–19.



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