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Breast cancer recurrence prediction using random forest model
conference contribution
posted on 2018-01-01, 00:00 authored by Tahsien Ali Hussein Al-Quraishi, Jemal AbawajyJemal Abawajy, Morshed ChowdhuryMorshed Chowdhury, Sutharshan RajasegararSutharshan Rajasegarar, A S AbdalradaBreast cancer is the second most common cause of death among Australian females. To reduce the probability of death, early detection and prevention of breast cancer is a crucial factor. Evaluating the probability of breast cancer recurrence is an important act related to breast cancer prognosis. The aim of this paper is to predict the probability of breast cancer recurrence among patients. The researchers individually applied Random Forest and Deep Neural Network classifiers to increase the prediction accuracy of those models. Wisconsin Prognosis Breast Cancer dataset was obtained from UCI machine learning Repository. The results of our experiment indicate that Random Forest technique achieved the highest accuracy compared to the existing works.
History
Event
Society for Clinical Data Management. Conference (3rd : 2018 : Johor, Malaysia)Volume
700Series
Society for Clinical Data Management ConferencePagination
318 - 329Publisher
SpringerLocation
Johor, MalaysiaPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2018-02-06End date
2018-02-07ISSN
2194-5357ISBN-13
9783319725499Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2018, Springer International Publishing AGEditor/Contributor(s)
Rozaida Ghazali, Mustafa Deris, Nazri Nawi, Jemal AbawajyTitle of proceedings
SCDM 2018 : Concise and informative : Proceedings of the 3rd International Conference on Soft Computing and Data MiningUsage metrics
Categories
Keywords
breast cancerrandom forestdeep neural networkScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Information SystemsComputer Science, Interdisciplinary ApplicationsComputer ScienceInformation SystemsArtificial Intelligence and Image ProcessingDistributed Computing
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