machine learning, end stage renal disease, mortality, hemodialysis
Health and Medical Administration | Nephrology
We examined machine learning methods to predict death within six months using data derived from the United States Renal Data System (USRDS). We specifically evaluated a generalized linear model, a support vector machine, a decision tree and a random forest evaluated within the context of K-10 fold validation using the CARET package available within the open source architecture R program. We compared these models with the feed forward neural network strategy that we previously reported on with this data set.
Khitan, Zeid; Jacob, Alexis D.; Balentine, Courtney; Jacob, Adam N.; Sanabria, Juan R.; and Shapiro, Joseph I.
"Predicting Adverse Outcomes in End Stage Renal Disease: Machine Learning Applied to the United States Renal Data System,"
Marshall Journal of Medicine:
4, Article 8.
Available at: https://mds.marshall.edu/mjm/vol4/iss4/8