Name
Title | ||
|---|---|---|
An Ensemble of Heterogeneous Incremental Classifiers for Assisted Reproductive Technology Outcome Prediction |
Abstract | ||
|---|---|---|
Machine learning (ML) is a futuristic concept, utilized as a tool for modeling real-world applications. Today, healthcare worldwide has drawn the attention of ML, with its ability to analyze huge data sets and convert information into clinical insights that aid physicians in disease diagnosis and treatment planning, leading to low cost, better outcomes, and greater patient satisfaction. One of suc... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCSS.2020.3032640 | IEEE Transactions on Computational Social Systems |
Keywords | DocType | Volume |
Subspace constraints,Predictive models,Medical services,Data models,Support vector machines,Prediction algorithms,Training | Journal | 8 |
Issue | ISSN | Citations |
3 | 2329-924X | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. Ranjini | 1 | 0 | 1.01 |
| A. Suruliandi | 2 | 7 | 5.50 |
| S. P. Raja | 3 | 12 | 6.67 |