Prediction models for estimation of survival rate and relapse for breast cancer patients

Cirkovic, Bojana R. Andjelkovic; Cvetković, Aleksandra; Ninkovic, Srdjan; Filipović, Nenad

doi:10.1109/bibe.2015.7367658

Cited by 19 publications

(16 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The two styles of arrows indicate two different predictions made using the nomogram. cancer relapse prediction model [32]. Adjuvant!…”

Section: Breast Cancer Recurrence Modelsmentioning

confidence: 99%

“…El-Serag et al used logistic regression models to predict the development of Hepatocellular Carcinoma (HCC), a form of liver cancer, within 6 months of an α-fetoprotein (AFP) test [37]. Among other models, Cirkovic et al built a logistic regression model to predict recurrence after surgery for breast cancer [32]. Bayati et al compare a traditional LR model to their own improved LR models based on multi-task learning, as their model attempts to predict risk of multiple different diseases (of which cancer is one) [8].…”

Section: Statistical Modelsmentioning

confidence: 99%

“…Radespiel-Tröger et al construct decision trees to model the recurrence of colon cancer within five years of curative resection [42]. Cirkovic et al and Ahmad et al both utilize a C4.5 model (among others) to predict recurrent breast cancer [32] [43].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…Kim et al ( Figure 5) provide a basic description of an SVM model in their paper that discriminates between recurrence and non-recurrence in breast cancer patients [4]. Tseng et al [41], Cirkovic et al [32], Liang et al [44], and Ahmad et al [43] all use SVMs to predict cancer recurrence.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

See 3 more Smart Citations

A review of statistical and machine learning methods for modeling cancer risk using structured clinical data

Richter

Khoshgoftaar

2018

Artificial Intelligence in Medicine

109

View full text Add to dashboard Cite

“…The two styles of arrows indicate two different predictions made using the nomogram. cancer relapse prediction model [32]. Adjuvant!…”

Section: Breast Cancer Recurrence Modelsmentioning

confidence: 99%

Section: Statistical Modelsmentioning

confidence: 99%

Section: Machine Learning Modelsmentioning

confidence: 99%

Section: Machine Learning Modelsmentioning

confidence: 99%

See 2 more Smart Citations

A review of statistical and machine learning methods for modeling cancer risk using structured clinical data

Richter

Khoshgoftaar

2018

Artificial Intelligence in Medicine

109

View full text Add to dashboard Cite

“…So the direction of research focuses on the identification of cancer in the early stages. B. R. A. Cirkovic, A. M. Cvetkovic, S. M. Ninkovic, and D. Nenad [6] described the practical application of data mining methods for estimation of survival rate and disease relapse for breast cancer patients. A comparative study of prominent machine learning models was carried out and according to the achieved results it was concluded that the classifiers obviously learn some of the concepts of breast cancer survivability and recurrence.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review on Data Mining Techniques for Prediction of Breast Cancer Recurrence

Padmapriya

Vadivu²

2019

SSRN Journal

View full text Add to dashboard Cite

The most common type of cancer in women worldwide is the Breast Cancer. Breast cancer may be detected early using Mammograms, probably before it's spread. Recurrent breast cancer could occur months or years after initial treatment. The cancer could return within the same place because the original cancer (local recurrence), or it may spread to different areas of your body (distant recurrence). Early stage treatment is done not only to cure breast cancer however additionally facilitate in preventing its repetition/recurrence. Data mining algorithms provide assistance in predicting the early-stage breast cancer that continually has been difficult analysis drawback. The projected analysis can establish the most effective algorithm that predicts the recurrence of the breast cancer and improve the accuracy the algorithms. Large information like Clump, Classification, Association Rules, Prediction and Neural Networks, Decision Trees can be analyzed using data mining applications and techniques.

show abstract

Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence

Silva

Lezama

Varela

et al. 2019

Green, Pervasive, and Cloud Computing

View full text Add to dashboard Cite

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.

show abstract

Prediction models for estimation of survival rate and relapse for breast cancer patients

Cited by 19 publications

References 10 publications

A review of statistical and machine learning methods for modeling cancer risk using structured clinical data

A review of statistical and machine learning methods for modeling cancer risk using structured clinical data

A Review on Data Mining Techniques for Prediction of Breast Cancer Recurrence

Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence

Contact Info

Product

Resources

About