Name
Abstract | ||
|---|---|---|
We are in an era of digital medicine in which physicians can generate copious patient data, but tools to analyse these data are limited. Thus, we used case data from patients with oesophageal cancer from a medical institution, removed incomplete information, and quantified the textual data according to recommendations from the corresponding physicians. We used different classification algorithms to process the data, predict patient survival, and compare accuracies across algorithms. Our experimental results show that the BayesNet algorithm was highly accurate and precise, and, thus, may represent a promising data-mining tool. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1504/IJCSE.2020.107348 | INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING |
Keywords | DocType | Volume |
data mining, classification algorithms, oesophageal cancer, BayesNet, digital medicine, patient data, patient survival | Journal | 22 |
Issue | ISSN | Citations |
2-3 | 1742-7185 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanning Cao | 1 | 0 | 0.68 |
| Xiaoshu Zhang | 2 | 0 | 0.68 |
| jin wang | 3 | 243 | 36.79 |