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The dataset from Kaggle was adopted to predict the breast tumor growth and sizes using random tree classification, logistic regression, XBoost tree and multilayer perceptron on the dataset. The implementation of these machine learning algorithms and visualization of the results was done using Python. The results achieved a high accuracy (99.65%) on training and\u00a0testing datasets which is far better than traditional means. The predictive model has good potential to enhance early detection and diagnosis of breast cancer and improvement of treatment outcome. It could also assist patients to timely deal with their condition or life patterns to support their recovery or survival.<\/jats:p>","DOI":"10.1142\/s1793962323410234","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T13:39:07Z","timestamp":1653053947000},"source":"Crossref","is-referenced-by-count":32,"title":["A hybrid machine learning model for timely prediction of breast cancer"],"prefix":"10.1142","volume":"14","author":[{"given":"Surjeet","family":"Dalal","sequence":"first","affiliation":[{"name":"College of Computing Science and IT, Teerthanker Mahaveer University, Moradabad, UP, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4067-3256","authenticated-orcid":false,"given":"Edeh Michael","family":"Onyema","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Coal City University, Enugu, Nigeria"},{"name":"Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India"}]},{"given":"Pawan","family":"Kumar","sequence":"additional","affiliation":[{"name":"College of Computing Science and IT, Teerthanker Mahaveer University, Moradabad, UP, India"}]},{"given":"Didiugwu Chizoba","family":"Maryann","sequence":"additional","affiliation":[{"name":"Department of Biological Science, Coal City University Enugu, Nigeria"}]},{"given":"Akindutire Opeyemi","family":"Roselyn","sequence":"additional","affiliation":[{"name":"Department of Statistics, Ekiti State University, Ekiti State, Nigeria"}]},{"given":"Mercy Ifeyinwa","family":"Obichili","sequence":"additional","affiliation":[{"name":"Department of Mass Communication, Alex Ekwueme Federal University, Ndufu-Alike Ikwo, Ebonyi State, Nigeria"}]}],"member":"219","published-online":{"date-parts":[[2022,6,25]]},"reference":[{"issue":"3","key":"S1793962323410234BIB001","first-page":"5250","volume":"8","author":"Neela A. 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