Name
Title | ||
|---|---|---|
Identifying people at risk of developing type 2 diabetes: A comparison of predictive analytics techniques and predictor variables. |
Abstract | ||
|---|---|---|
•The study aims to identify the patients at risk of type 2 diabetes (T2D).•The study compares the performance of machine learning classification algorithms.•A list of predictors for T2D for short, medium and long term is provided.•Period and Purpose of prediction of T2D influence the performance. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ijmedinf.2018.08.008 | International Journal of Medical Informatics |
Keywords | Field | DocType |
Classification Algorithms,Machine Learning,Diabetes | Data mining,Predictive analytics,Risk analysis (business),Support vector machine,Sampling (statistics),Artificial intelligence,Artificial neural network,Statistical classification,Analytics,Medicine,Logistic regression,Machine learning | Journal |
Volume | ISSN | Citations |
119 | 1386-5056 | 2 |
PageRank | References | Authors |
0.40 | 24 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amir Talaei-Khoei | 1 | 52 | 15.63 |
| James M. Wilson | 2 | 3 | 1.09 |