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Prediction of Survival in Breast Cancer Patients using Random Forest Classifier and ReliefF Feature Selection Method

Diogo Albino De Queiroz, Gabriel Sousa Almeida Assunção, Kamila Alves Da Silva Ferreira, Vilian Veloso De Moura Fé, Vitória Paglione Balestero De Lima, Fernanda Antunes Dias, Túlio Couto Medeiros, Karen Nayara De Souza Braz, Rodrigo Augusto Rosa Siviero, Pâmela Alegranci, Eveline Aparecida Isquierdo Fonseca De Queiroz
2021 Zenodo  
Keywords- classification; prediction; breast cancer patients; Random Forest classifier; ReliefF method.  ...  Random Forest has demonstrated to be a relevant technique in predicting survival based on staging, treatment, prognosis, and patient characteristics.  ...  Figure 5 . 5 Survival prediction results. A) Results using the Random Forest classifier.  ... 
doi:10.5281/zenodo.4898152 fatcat:fmr7dkb5qngp3htvrgcpq4rd3u

Predictive value of initial FDG-PET features for treatment response and survival in esophageal cancer patients treated with chemo-radiation therapy using a random forest classifier

Paul Desbordes, Su Ruan, Romain Modzelewski, Pascal Pineau, Sébastien Vauclin, Pierrick Gouel, Pierre Michel, Frédéric Di Fiore, Pierre Vera, Isabelle Gardin, Aamir Ahmad
2017 PLoS ONE  
To select the best features, the use of a random forest (RF) classifier was investigated.  ...  Methods Sixty-five patients with an esophageal cancer treated with a combined chemo-radiation therapy were retrospectively included. All patients underwent a pretreatment whole-body FDG-PET.  ...  Given the number of features (107) and the number of patients (56), a feature selection was done based on a random survival forest.  ... 
doi:10.1371/journal.pone.0173208 pmid:28282392 pmcid:PMC5345816 fatcat:jsd7evhgmzf6vnf5xtvbrtfzvi

Prognostic Prediction of Lung Cancer Patients Using Random Survival Forest
Random Survival Forest を用いた肺がん患者の予後予測

Medical Imaging and Information Sciences  
Cox regression model and random survival forest (RSF) with the selected 4 radiomic features were employed for estimating the survivor functions of 67 patients.  ...  Our proposed method for the prognostic prediction of lung cancer patients can provide useful information in formulating patients' treatment plans.  ...  ., Kogalur U.B., Blackstone E.H. et al., Random survival forests, Ann.  ... 
doi:10.11318/mii.36.93 fatcat:kv2fayn2dnbmng7bbuzinpmope

Prediction the survival of patients with breast cancer using random survival forests for competing risks

Roya Najafi-Vosough, Javad Faradmal, Leili Tapak, Behnaz Alafchi, Khadijeh Najafi Ghobadi, Tayeb Mohammadi
The purpose of this study was to identify important prognosis factors associated with survival duration among patients with BC using random survival forests (RSF) model in presence of competing risks.  ...  The cause-specific Cox proportional hazards and RSF models were employed to determine the important risk factors for survival of the patients.  ...  NoN CommuNiCable Diseases Prediction the survival of patients with breast cancer using random survival forests for competing risks province in Iran, during 2012-2015.  ... 
doi:10.15167/2421-4248/jpmh2022.63.2.2405 pmid:35968067 pmcid:PMC9351408 fatcat:zx45g7lz7nap5pjtjhdttj3cny

Predicting Epithelial Ovarian Cancer First Recurrence with Random Survival Forest: Comparison Parametric, Semi-Parametric, and Random Survival Forest Methods

Maryam Deldar, Robab Anbiaee, Kourosh Sayehmiri
2021 Journal of biostatistics and epidemiology  
This study compares the prediction error of random survival forest with Cox and Weibull models in predicting the time to the first recurrence in patients with epithelial ovarian cancer.  ...  Conclusion: Random survival forest with a suitable fit on many variables and without the need for a special default with a prediction error less than the Weibull and Cox methods can predict the response  ...  Ghodratollah Roshanaee (2018) determined the factors affecting on survival of kidney transplant in living donor patients using a random survival forest, based on Berier score the prediction error of random  ... 
doi:10.18502/jbe.v6i4.5680 fatcat:2y26z24fzncnlib7sf4tevhxwq

Application of data mining in the provision of in-home medical care for patients with advanced cancer

Chao Yang, Ruihua Yu, Hui Ji, Haosheng Jiang, Wanli Yang, Feng Jiang
2021 Translational Cancer Research  
The medical expenses and survival time of the patients were classified and predicted through the use of random forest algorithms, support-vector machine algorithms, and back-propagation (BP) neural network  ...  The random forest algorithm is the most suitable prediction model for predicting medical costs and patient survival with the quantity of data currently available.  ...  Our results showed that the random forest algorithm had an accuracy of more than 80% for predicting patients' survival times, and it also had high accuracy in the prediction of the medical costs of home  ... 
doi:10.21037/tcr-21-896 pmid:35116609 pmcid:PMC8798724 fatcat:xugeykyusvdkzfntwxu2lmoql4

Predicting Factors Affecting the First Recurrence of Epithelial Ovarian Cancer Using Random Survival Forest

Maryam Deldar, Robab Anbiaee, Kourosh Sayehmiri
2021 Acta Medica Iranica  
Random Survival Forest was fitted to the data to investigate the key factors affecting the first recurrence of epithelial ovarian cancer.  ...  Random Survival Forest by repeated tree construction on Bootstrap samples and averaging on the results of these trees reduce the prediction error and cause further generalization of these results.  ...  This study investigates random survival forests in predicting first recurrence in patients with epithelial ovarian cancer.  ... 
doi:10.18502/acta.v59i8.7258 fatcat:43oaf2va2zd6nioxm4rrf2p74i

Prediction of90Y Radioembolization Outcome from Pretherapeutic Factors with Random Survival Forests

Michael Ingrisch, Franziska Schöppe, Karolin Paprottka, Matthias Fabritius, Frederik F. Strobl, Enrico N. De Toni, Harun Ilhan, Andrei Todica, Marlies Michl, Philipp Marius Paprottka
2017 Journal of Nuclear Medicine  
approach based on random survival forests.  ...  Conclusion: Random survival forests are a simple and straightforward machine-learning approach for prediction of overall survival.  ...  DISCUSSION In this study, we have used an advanced statistical method, random survival forests, to predict response to 90 Y radioembolization FIGURE 1.  ... 
doi:10.2967/jnumed.117.200758 pmid:29146692 fatcat:6akujxy3wnduxjv4srq5u2qiv4

Identifying Stage II Colorectal Cancer Recurrence Associated Genes by Microarray Meta-Analysis and Building Predictive Models with Machine Learning Algorithms

Wei Lu, Xiang Pan, Siqi Dai, Dongliang Fu, Maxwell Hwang, Yingshuang Zhu, Lina Zhang, Jingsun Wei, Xiangxing Kong, Jun Li, Qian Xiao, Kefeng Ding (+1 others)
2021 Journal of Oncology  
The random survival forest model which was based on the recurrence associated gene signature could strongly predict the recurrence risk of stage II colorectal cancer patients.  ...  Stage II colorectal cancer patients had heterogeneous prognosis, and patients with recurrent events had poor survival.  ...  Finally, with the trained random survival forest model, recurrence risk scores of patients were calculated using the "predict" function of the "stats" package [25] . e random survival forest model was  ... 
doi:10.1155/2021/6657397 pmid:33628243 pmcid:PMC7889382 fatcat:obxcqp2n5vem3foelrlyugkb4i

Gene Selection Based On an Improved Iterative Feature Elimination Random Survival Forest

Ting Wai Soon, Kohbalan Moorthy, Mohd Saberi Mohamad, Safaai Deris, Sigeru Omatu
2015 Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications  
The survival prediction has become important for patient cancer treatment plan.  ...  A Random Survival Forest method has been selected to perform gene selection for survival prediction.  ...  Identification of high prognostic values can help cancer patient treatment in clinical study. It is important to construct useful information for patient care.  ... 
doi:10.5687/sss.2015.124 fatcat:v5wlwcek5fextlkammrtwucwpy

Pathway analysis using random forests with bivariate node-split for survival outcomes

Herbert Pang, Debayan Datta, Hongyu Zhao
2009 Computer applications in the biosciences : CABIOS  
Results: In this article, we describe a pathway-based method using random forests to correlate gene expression data with survival outcomes and introduce a novel bivariate node-splitting random survival  ...  We compared different implementations of random forests with different split criteria and found that bivariate nodesplitting random survival forests with log-rank test is among the best.  ...  Pawitan et al. breast cancer dataset We considered 435 gene sets for random forests prediction on breast cancer survival outcomes.  ... 
doi:10.1093/bioinformatics/btp640 pmid:19933158 pmcid:PMC2804301 fatcat:wekexdiby5avdeinex5g3zvbfq

Design and implementation of cancer patient survival prediction model based on ensemble learning method

Bi Chuanmei, Fang Xiaonan, Geng Zezheng, Dong Ling, T. Zhu, M. Anpo, A. Sharifi
2021 E3S Web of Conferences  
Then use a random forest ensemble learning algorithm to train it to get a preliminary model.  ...  To study the impact of genomics on cancer diseases, bioinformatics and integrated learning methods are used to conduct survival analysis on colon cancer and rectal cancer data in the cancer gene map database  ...  Random Survival Forest Model The two data sets were used to establish a random forest model for survival analysis.  ... 
doi:10.1051/e3sconf/202127104030 fatcat:jh7hwrybyjc5dkluprdapbvnrq

Predicting factors for survival of breast cancer patients using machine learning techniques

Mogana Darshini Ganggayah, Nur Aishah Taib, Yip Cheng Har, Pietro Lio, Sarinder Kaur Dhillon
2019 BMC Medical Informatics and Decision Making  
In determining the significant prognostic factors of breast cancer survival rate, prediction models were built using decision tree, random forest, neural networks, extreme boost, logistic regression, and  ...  Next, the dataset was clustered based on the receptor status of breast cancer patients identified via immunohistochemistry to perform advanced modelling using random forest.  ...  However, the (R version 3.5.1) source codes used to analyse breast cancer survival rate using machine learning techniques are deposited in GitHub ( Machine-Learning-on-Breast-Cancer-Survival-Prediction  ... 
doi:10.1186/s12911-019-0801-4 fatcat:5j36b753vffyzdswu65zqetrpu

Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial

Raghav Sundar, Nesaretnam Barr Kumarakulasinghe, Yiong Huak Chan, Kazuhiro Yoshida, Takaki Yoshikawa, Yohei Miyagi, Yasushi Rino, Munetaka Masuda, Jia Guan, Junichi Sakamoto, Shiro Tanaka, Angie Lay-Keng Tan (+9 others)
2021 Gut  
SAMIT GC samples were profiled using a customised 476 gene NanoString panel. A random forest machine-learning model was applied on the NanoString profiles to develop a gene signature.  ...  ObjectiveTo date, there are no predictive biomarkers to guide selection of patients with gastric cancer (GC) who benefit from paclitaxel.  ...  These findings suggest that the random-forest gene signature is predictive for survival benefit with paclitaxel treatment and is not merely a prognostic biomarker.  ... 
doi:10.1136/gutjnl-2021-324060 pmid:33980610 pmcid:PMC8921574 fatcat:ocbvk4dhvnd2tas7kzskdjf5fu

Prediction of early breast cancer patient survival using ensembles of hypoxia signatures

Inna Y. Gong, Natalie S. Fox, Vincent Huang, Paul C. Boutros, Rajeev Samant
2018 PLoS ONE  
Here, we systematically evaluated machine-learning ensembles of preprocessing methods as a general strategy to improve biomarker performance for prediction of survival from early breast cancer.  ...  of early breast cancer patient survival using ensembles of hypoxia signatures.  ...  Random forest classifiers were constructed to predict prognosis for individual patients using combinations of the ensemble of 24 preprocessing pipeline predictions and the engineered features.  ... 
doi:10.1371/journal.pone.0204123 pmid:30216362 pmcid:PMC6138385 fatcat:ddnuiegscfhxphryek5ug4pd6a
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