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Proximate breast cancer factors using data mining classification techniques.

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dc.contributor.author Mensah, A. C.
dc.contributor.author Asare, I. O.
dc.date.accessioned 2022-08-24T10:35:23Z
dc.date.available 2022-08-24T10:35:23Z
dc.date.issued 2019
dc.identifier.other 10.4018/IJBDAH.2019010104
dc.identifier.uri https://www.igi-global.com/gateway/article/full-text-pdf/232335&riu=true
dc.identifier.uri http://atuspace.atu.edu.gh:8080/handle/123456789/135
dc.description.abstract Breast cancer is the most common of all cancers and is the leading cause of cancer deaths in women worldwide. The classification of breast cancer data can be useful to predict the outcome of some diseases or discover the genetic behavior of tumors. Data mining technology helps in classifying cancer patients and this technique helps to identify potential cancer patients by simply analyzing the data. This study examines the determinant factors of breast cancer and measures the breast cancer patient data to build a useful classification model using a data mining approach. In this study of 2397 women, 1022 (42.64%) were diagnosed with breast cancer. Among the four main learning techniques such as: Random Forest, Naive Bayes, Classification and Regression Model (CART), and Boosted Tree model were used for the study. The Random Forest technique had the better accuracy value of 0.9892(95%CI,0.9832 -0.9935) and a sensitivity value of about 92%. This means that the Random Forest learning model is the best model to classify and predict breast cancer based on associated factors. en_US
dc.language.iso en en_US
dc.publisher IGI Global en_US
dc.relation.ispartofseries vol;4
dc.subject Boosted Tree Model en_US
dc.subject Breast Cancer en_US
dc.subject Classification and Regression Model en_US
dc.subject Naive Bayes en_US
dc.subject Random Forest en_US
dc.title Proximate breast cancer factors using data mining classification techniques. en_US
dc.type Article en_US


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