If the response variable is binary such as whether a patient developed HCC or not, then a classification tree is generated that predicts the probability of developing HCC.
We computed a propensity score, which predicted the probability of developing AKI conditional on the observed baseline covariates, using multivariable logistic regression.
A model to predict the probability of invasion was developed and subsequently simplified to divide patients into two risk categories.
The model predicted that the probability of developing TE is 63.9% for patients with all four at-risk factors.
If the risk factors of CKD are known, one should be able to predict the probability of at risk individuals developing CKD, and thus identify at risk populations.
In the second part of our analysis (i.e. Disease Evolution Monitoring), we aim to predict the probability of a patient to develop a relapse during the follow-up period; hence, we introduce the time dimension and estimate the approximate timing that a relapse is more likely to occur.
Using these results, a model to predict the probability of having invasive cancer was developed.
Logistic regression models were developed to predict the probability of success with limited intervention and CDT.
Mathematical models have been developed to predict the probability of growth of spoilage moulds in response to various preservative systems in ready to drink beverages.
