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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  
As an alternative, this study used machine learning techniques to build models for detecting and visualising significant prognostic indicators of breast cancer survival rate.  ...  Conclusion: Interestingly the various machine learning algorithms used in this study yielded close accuracy hence these methods could be used as alternative predictive tools in the breast cancer survival  ...  However, the (R version 3.5.1) source codes used to analyse breast cancer survival rate using machine learning techniques are deposited in GitHub (https://github.com/MoganaD/ Machine-Learning-on-Breast-Cancer-Survival-Prediction  ... 
doi:10.1186/s12911-019-0801-4 fatcat:5j36b753vffyzdswu65zqetrpu

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

R.S.PadmaPriya, P. Senthil Vadivu
2019 Zenodo  
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.  ...  Large information like Clump, Classification, Association Rules, Prediction and Neural Networks, Decision Trees can be analyzed using data mining applications and techniques.  ...  Three machine learning algorithms were applied in order to predict breast cancer survivability.  ... 
doi:10.5281/zenodo.3355235 fatcat:wozy2rjnsncfncyj3oylhsojey

Predication of cancer disease using machine learning approach

F.J. Shaikh, D.S. Rao
2021 Materials Today: Proceedings  
Many of these methods are widely used for the development of predictive models for predicating a cure for cancer, some of the methods are artificial neural networks (ANNs), support vector machine (SVMs  ...  These techniques have therefore been used as a model for the development and treatment of cancer. As, it is important that ML instruments are capable of detecting key features from complex datasets.  ...  So, for predicting lung cancer in an efficient manner with the help of improved machine learning techniques can be use.  ... 
doi:10.1016/j.matpr.2021.03.625 fatcat:meq7nl6gsfhufozyx62uam7woe

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  
Studies have evaluated the use of machine learning to support clinical evaluation in cancer patients.  ...  Therefore, this model could be recommended as a useful tool to predict the survival rate of breast cancer patients and to support medical decisions.  ...  DECLARATION OF INTEREST STATEMENT The authors declare no conflicts of interest.  ... 
doi:10.5281/zenodo.4898152 fatcat:fmr7dkb5qngp3htvrgcpq4rd3u

Comparative Study of Data Mining and Statistical Learning Techniques for Prediction of Cancer Survivability

Charles Edeki, Shardul Pandya
2012 Mediterranean Journal of Social Sciences  
Six data mining and statistical learning techniques were applied to breast cancer datasets for survival analysis.  ...  cancer survivability and prognosis using R statistical computing tool and Weka machine learning tool (freely available open source software applications).  ...  The main focus of this research was to study the effective classification ~ 50 ~ learning techniques for prediction of breast cancer survivability.  ... 
doaj:3e0a30d2c24a479b9380193efc4b9945 fatcat:d45tow7ioreipbisnwrzokhkpi

Machine Learning With K-Means Dimensional Reduction for Predicting Survival Outcomes in Patients With Breast Cancer

Melissa Zhao, Yushi Tang, Hyunkyung Kim, Kohei Hasegawa
2018 Cancer Informatics  
Using machine learning methods to construct predictive models for 5-year survival in patients with breast cancer, we demonstrated discrimination ability across models with new insight into the stability  ...  and utility of dimensional reduction on genomic features in breast cancer survival prediction.  ...  Another study by Vanneschi et al 25 examined machine learning techniques for breast cancer survival using gene signatures alone.  ... 
doi:10.1177/1176935118810215 pmid:30455569 pmcid:PMC6238199 fatcat:ube3oac7j5e7pghwkz3qjwtry4

Prediction of Metastatic Relapse in Breast Cancer using Machine Learning Classifiers

Ertel Merouane, Amali Said, El Faddouli Nour-eddine
2022 International Journal of Advanced Computer Science and Applications  
The current study looked into using CRISP-DM machine learning algorithms to predict metastatic recurrence in patients with early-stage (non-metastatic) breast cancer so that treatmentappropriate medicine  ...  To create predictive models, we used machine learning techniques such as Support Vector Machine (SVM), Nave Bayes (NB), K-Nearest Neighbors (KNN) and Logistic Regression (LR).  ...  Regression (LR) and Linear Discriminant Analysis (LDA), for the prediction of breast cancer survival and metastasis.  ... 
doi:10.14569/ijacsa.2022.0130222 fatcat:aqn4vp75q5axlhfcy65rzyrztu

Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry

Sunil Gupta, Truyen Tran, Wei Luo, Dinh Phung, Richard Lee Kennedy, Adam Broad, David Campbell, David Kipp, Madhu Singh, Mustafa Khasraw, Leigh Matheson, David M Ashley (+1 others)
2014 BMJ Open  
Disease-specific data from a purpose-built cancer registry (Evaluation of Cancer Outcomes (ECO)) from 869 patients were used to predict survival at 6, 12 and 24 months.  ...  Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning  ...  patient care, monitoring resource utilisation and improving Strengths and limitations of this study ▪ This is the first study using machine learning of administrative and registry data for cancer survival  ... 
doi:10.1136/bmjopen-2013-004007 pmid:24643167 pmcid:PMC3963101 fatcat:keqywacklndflayedyldxryvjm

Feasibility Study on Data Mining Techniques in Diagnosis of Breast Cancer

Keerthana Rajendran, Asia Pacific University of Technology & Innovation, Kuala Lumpur 57000, Malaysia, Manoj Jayabalan, Vinesh Thiruchelvam, V. Sivakumar
2019 International Journal of Machine Learning and Computing  
Abstract-Survivability of patients suffering from breast cancer varies according to the stages. The early detection of breast cancer increase the longevity of patients.  ...  This study reviews article provides a holistic view of the types of data mining techniques used in prediction of breast cancer.  ...  The survivability rate of the patients differs due to varying factors. In [21] proposed a model to predict the 5-year survivability of breast cancer using SEER dataset.  ... 
doi:10.18178/ijmlc.2019.9.3.806 fatcat:emdwvc33jjbt3lyoblcjcspgoq

An Artificial Intelligence-Enabled Pipeline for Medical Domain: Malaysian Breast Cancer Survivorship Cohort as a Case Study

Mogana Darshini Ganggayah, Sarinder Kaur Dhillon, Tania Islam, Foad Kalhor, Teh Chean Chiang, Elham Yousef Kalafi, Nur Aishah Taib
2021 Diagnostics  
The aim of this study is to propose a pipeline to develop a fully automated clinician-friendly AI-enabled database platform for breast cancer survival prediction.  ...  A case study of breast cancer survival cohort from the University Malaya Medical Centre was used to develop and evaluate the pipeline.  ...  The authors also thank the members of the Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, University of Malaya for their continuous support to complete this study.  ... 
doi:10.3390/diagnostics11081492 fatcat:mq6nrg3hu5c7rpyqjgrlvol3q4

Supervised Machine Learning Predictive Analytics For Triple-Negative Breast Cancer Death Outcomes

Yucan Xu, Lingsha Ju, Jianhua Tong, Chengmao Zhou, Jianjun Yang
2019 OncoTargets and Therapy  
Machine learning was used to predict the death outcomes of patients with triple-negative breast cancer, 5 years after discharge.  ...  To use machine learning algorithms to predict the death outcomes of patients with triple-negative breast cancer, 5 years after discharge. 1570 stage I-III breast cancer patients receiving treatment from  ...  We are grateful to Professor Fengxi Su for sharing his data 32 and allowing us to use them for research.  ... 
doi:10.2147/ott.s223603 pmid:31802913 pmcid:PMC6830358 fatcat:mhkaqo6xcrgwhmqpzxhq2oov6y

The use of Decision Tree in Breast Cancer-Related Research: a Scoping Analysis Based on Scopus-Indexed Articles

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Breast cancer is the leading cancer that occurs in women globally. The use of machine learning has been introduced to supplement the work in breast cancer studies.  ...  Even though the number of articles found is adequate, several categories of breast cancer are lacking in publications specifically the survivability, incidence, and recurrence of breast cancer among patients  ...  Ahmad Aidil Arafat Dzulkarnain for the review and valuable suggestions that improved the paper.  ... 
doi:10.35940/ijitee.i3290.0789s319 fatcat:mvilfnwvpjbtfpb7gvqzsxpswa

Breast Cancer Diagnosis Using Machine Learning Algorithms - A Survey

Gayathri B.M, Sumathi C.P, Santhanam T
2013 International Journal of Distributed and Parallel systems  
This paper summarizes the survey on breast cancer diagnosis using various machine learning algorithms and methods, which are used to improve the accuracy of predicting cancer.  ...  Waiting for diagnosing a breast cancer for a long time may increase the possibility of the cancer spreading.  ...  Table 2 shows that SVM technique is used for detecting breast cancer. But Relevance vector machine (RVM) gives more accurate results than support vector machines.  ... 
doi:10.5121/ijdps.2013.4309 fatcat:kfvydgzyqnhlnfj2p7s53ba6zu

Analyzing Potential of SVM Based Classifiers for Intelligent and Less Invasive Breast Cancer Prognosis

Amna Ali, Umer Khan, Ali Tufail, Minkoo Kim
2010 2010 Second International Conference on Computer Engineering and Applications  
In this paper, we have targeted this strength of SVMs to analyze the potential of classification through feature vectors for predicting the survival chances of a breast cancer patient.  ...  Cancer prognosis estimates recurrence of disease and predict survival of patient; hence resulting in improved patient management.  ...  potential of Support vector Machines for prediction of breast cancer survivability in particular, and breast cancer prognosis in general.  ... 
doi:10.1109/iccea.2010.212 fatcat:vzggubifsbfzfe3sbisweo3mhe

Breast Cancer Detection with Machine Learning

Manav Mangukiya
2022 International Journal for Research in Applied Science and Engineering Technology  
Our aim is to review various Techniques To detect early, efficiently and accurately Using Machine Learning.  ...  Early diagnosis of this helps to prevent the cancer. If breast cancer is detected in early stage, then Survival rate is very high. Machine Learning methods are effective ways to classify data.  ...  We used all Methodologies to Predict the result and Noted their Accuracy. CONCLUSION & FUTURE WORK This paper examined different machine learning techniques for detection of breast cancer.  ... 
doi:10.22214/ijraset.2022.40204 fatcat:klhn4k7jnfck3mnqxxznqzr7uy
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