Name
Title | ||
|---|---|---|
Integration Of Data Mining Classification Techniques And Ensemble Learning For Predicting The Type Of Breast Cancer Recurrence |
Abstract | ||
|---|---|---|
Conservative surgery plus radiotherapy is an alternative to radical mastectomy in the early stages of breast cancer, presenting equivalent survival rates. Data mining facilitates to manage the data and provide the useful medical progression and treatment of cancerous conditions as these methods can help to reduce the number of false positive and false negative decisions. Various machine learning techniques can be used to support the doctors in effective and accurate decision making. In this paper, various classifiers have been tested for the prediction of type of breast cancer recurrence and the results show that neural networks outperform others. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-19223-5_2 | GREEN, PERVASIVE, AND CLOUD COMPUTING, GPC 2019 |
Keywords | Field | DocType |
Breast cancer, Recurrence events, Nonrecurrence events, K-means clustering | k-means clustering,Data mining,Breast cancer,Computer science,Radiation therapy,Radical mastectomy,Artificial neural network,Ensemble learning | Conference |
Volume | ISSN | Citations |
11484 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jesús Silva | 1 | 0 | 3.38 |
| Omar Bonerge Pineda Lezama | 2 | 0 | 1.35 |
| Noel Varela | 3 | 0 | 1.69 |
| Luz Adriana Borrero | 4 | 0 | 0.34 |