A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
Predicting factors for survival of breast cancer patients using machine learning techniques
2019
BMC Medical Informatics and Decision Making
Preserved Fulltext
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
2019
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2021
Materials Today: Proceedings
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2021
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2012
Mediterranean Journal of Social Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2018
Cancer Informatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2022
International Journal of Advanced Computer Science and Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2014
BMJ Open
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2019
International Journal of Machine Learning and Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2021
Diagnostics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2019
OncoTargets and Therapy
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2013
International Journal of Distributed and Parallel systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
2010
2010 Second International Conference on Computer Engineering and Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2014; you can also visit the original URL.
The file type is application/pdf.
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
2022
International Journal for Research in Applied Science and Engineering Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 10,435 results