An Ensemble Classifier for Predicting the Onset of Type II Diabetes
This work addresses early diagnosis for patients at risk of type II diabetes, but it is incremental as it applies standard ensemble methods to existing data.
The study tackled predicting the onset of type II diabetes using NHANES questionnaire data, achieving an AUC of 0.834 with an ensemble model.
Prediction of disease onset from patient survey and lifestyle data is quickly becoming an important tool for diagnosing a disease before it progresses. In this study, data from the National Health and Nutrition Examination Survey (NHANES) questionnaire is used to predict the onset of type II diabetes. An ensemble model using the output of five classification algorithms was developed to predict the onset on diabetes based on 16 features. The ensemble model had an AUC of 0.834 indicating high performance.