Research Paper on MODELS FOR PREDICTING PATIENT SURVIVAL AFTER LIVER TRANSPLANTATION USING MACHINE LEARNING

Description

Title: MODELS FOR PREDICTING PATIENT SURVIVAL AFTER LIVER TRANSPLANTATION USING MACHINE LEARNING

Abstract: In our research, we developed models that can foretell whether a patient will experience organ loss following a liver transplant within a given time frame. We developed predictors by using observations of bilirubin and creatinine throughout the first year following the transplantation, capturing both their static value and their variability. Our models do in fact have the predictive ability to demonstrate the benefit of accounting for measurement variability in biochemical processes, and this is the first contribution of our paper. In our second contribution, we found that despite having the best predictive power, which is crucial in medicine, full-complexity models like random forests and gradient boosting lack adequate interpretability. In our research, we discovered that generalized additive models (GAM) offer the desired interpretability and that their predictive power is superior to that of simple linear models.

Keywords: machine learning, models interpretability, survival prediction, generalized additive models, liver transplant

Paper Quality: SCOPUS / Web of Science Level Research Paper

Paper type: Analysis Based Research Paper

Subject: Computer Science

Writer Experience: 20+ Years

Plagiarism Report: Turnitin Plagiarism Report will be less than 10%

Restriction: Only one author may purchase a single paper. The paper will then indicate that it is out of stock.

What will I get after the purchase?

A turnitin plagiarism report of less than 10% in a pdf file and a full research paper in a word document.

In case you have any questions related to this research paper, please feel free to call/ WhatsApp on +919726999915