Prediction of heart failures for diabetics only using major longitudinal lab test results

In the presence of a large data set of electronic health records (EHRs), predicting the future diabetic complications is of importance for decision making in the medical treatments. Using modern machine learning techniques, it is generally becoming easier to build complex models to predict the future. While complex models are giving good prediction performance, simple models should be more useful in terms of interpretability and practicality in the real medical fields. For example the less the explanatory variables such as lab tests are used for the model, the more useful it is. Our interests thus lie in whether accurate prediction is possible when using less and major explanatory variables to make a model to predict the future diabetic complications.