FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2894844737> ?p ?o ?g. }

• W2894844737 endingPage "299" @default.
• W2894844737 startingPage "293" @default.
• W2894844737 abstract "<h2>Abstract</h2><h3>Background</h3> Breast cancer (BC) is one of the most common malignancies in women. Early diagnosis of BC and metastasis among the patients based on an accurate system can increase survival of the patients to >86%. This study aimed to compare the performance of six machine learning techniques two traditional methods for the prediction of BC survival and metastasis. <h3>Methods</h3> We used a dataset that include the records of 550 breast cancer patients. Naive Bayes (NB), Random Forest (RF), AdaBoost, Support Vector Machine (SVM), Least-square SVM (LSSVM) and Adabag, Logistic Regression (LR) and Linear Discriminant Analysis were used for the prediction of breast cancer survival and metastasis. The performance of the used techniques was evaluated with sensitivity, specificity, likelihood ratio and total accuracy. <h3>Results</h3> Out of 550 patients, 83.4% were alive and 85% did not experience metastasis. In prediction of survival, the average specificity of all techniques was ≥94% and the SVM and LDA have greater sensitivity (73%) in comparison to other techniques. The greater total accuracy (93%) belonged to the SVM and LDA. For metastasis prediction, the RF had the highest specificity (98%), the NB had highest sensitivity (36%) and the LR and LDA had the highest total accuracy (86%). <h3>Conclusions</h3> Our finding showed that the SVM outperformed other machine learning methods in prediction of survival of the patients in terms of several criteria. Nevertheless, the LDA technique as a classical method showed similar performance." @default.
• W2894844737 created "2018-10-12" @default.
• W2894844737 creator A5014085861 @default.
• W2894844737 creator A5017567045 @default.
• W2894844737 creator A5041046560 @default.
• W2894844737 creator A5061817155 @default.
• W2894844737 creator A5071595030 @default.
• W2894844737 creator A5078630563 @default.
• W2894844737 date "2019-09-01" @default.
• W2894844737 modified "2023-10-12" @default.
• W2894844737 title "Prediction of survival and metastasis in breast cancer patients using machine learning classifiers" @default.
• W2894844737 cites W1915129189 @default.
• W2894844737 cites W1970523565 @default.
• W2894844737 cites W1996029688 @default.
• W2894844737 cites W2012316318 @default.
• W2894844737 cites W2012518830 @default.
• W2894844737 cites W2019969379 @default.
• W2894844737 cites W2034679613 @default.
• W2894844737 cites W2054144613 @default.
• W2894844737 cites W2069914810 @default.
• W2894844737 cites W2071379517 @default.
• W2894844737 cites W2076723282 @default.
• W2894844737 cites W2124426131 @default.
• W2894844737 cites W2138974799 @default.
• W2894844737 cites W2168901348 @default.
• W2894844737 cites W2171316676 @default.
• W2894844737 cites W2328733597 @default.
• W2894844737 cites W2808354127 @default.
• W2894844737 cites W2911964244 @default.
• W2894844737 cites W3123209302 @default.
• W2894844737 doi "https://doi.org/10.1016/j.cegh.2018.10.003" @default.
• W2894844737 hasPublicationYear "2019" @default.
• W2894844737 type Work @default.
• W2894844737 sameAs 2894844737 @default.
• W2894844737 citedByCount "48" @default.
• W2894844737 countsByYear W28948447372019 @default.
• W2894844737 countsByYear W28948447372020 @default.
• W2894844737 countsByYear W28948447372021 @default.
• W2894844737 countsByYear W28948447372022 @default.
• W2894844737 countsByYear W28948447372023 @default.
• W2894844737 crossrefType "journal-article" @default.
• W2894844737 hasAuthorship W2894844737A5014085861 @default.
• W2894844737 hasAuthorship W2894844737A5017567045 @default.
• W2894844737 hasAuthorship W2894844737A5041046560 @default.
• W2894844737 hasAuthorship W2894844737A5061817155 @default.
• W2894844737 hasAuthorship W2894844737A5071595030 @default.
• W2894844737 hasAuthorship W2894844737A5078630563 @default.
• W2894844737 hasBestOaLocation W28948447371 @default.
• W2894844737 hasConcept C119857082 @default.
• W2894844737 hasConcept C121608353 @default.
• W2894844737 hasConcept C12267149 @default.
• W2894844737 hasConcept C126322002 @default.
• W2894844737 hasConcept C141404830 @default.
• W2894844737 hasConcept C143998085 @default.
• W2894844737 hasConcept C151956035 @default.
• W2894844737 hasConcept C153180895 @default.
• W2894844737 hasConcept C154945302 @default.
• W2894844737 hasConcept C169258074 @default.
• W2894844737 hasConcept C2779013556 @default.
• W2894844737 hasConcept C41008148 @default.
• W2894844737 hasConcept C52001869 @default.
• W2894844737 hasConcept C530470458 @default.
• W2894844737 hasConcept C69738355 @default.
• W2894844737 hasConcept C71924100 @default.
• W2894844737 hasConceptScore W2894844737C119857082 @default.
• W2894844737 hasConceptScore W2894844737C121608353 @default.
• W2894844737 hasConceptScore W2894844737C12267149 @default.
• W2894844737 hasConceptScore W2894844737C126322002 @default.
• W2894844737 hasConceptScore W2894844737C141404830 @default.
• W2894844737 hasConceptScore W2894844737C143998085 @default.
• W2894844737 hasConceptScore W2894844737C151956035 @default.
• W2894844737 hasConceptScore W2894844737C153180895 @default.
• W2894844737 hasConceptScore W2894844737C154945302 @default.
• W2894844737 hasConceptScore W2894844737C169258074 @default.
• W2894844737 hasConceptScore W2894844737C2779013556 @default.
• W2894844737 hasConceptScore W2894844737C41008148 @default.
• W2894844737 hasConceptScore W2894844737C52001869 @default.
• W2894844737 hasConceptScore W2894844737C530470458 @default.
• W2894844737 hasConceptScore W2894844737C69738355 @default.
• W2894844737 hasConceptScore W2894844737C71924100 @default.
• W2894844737 hasIssue "3" @default.
• W2894844737 hasLocation W28948447371 @default.
• W2894844737 hasLocation W28948447372 @default.
• W2894844737 hasOpenAccess W2894844737 @default.
• W2894844737 hasPrimaryLocation W28948447371 @default.
• W2894844737 hasRelatedWork W2979979539 @default.
• W2894844737 hasRelatedWork W3126015411 @default.
• W2894844737 hasRelatedWork W3204641204 @default.
• W2894844737 hasRelatedWork W4200057378 @default.
• W2894844737 hasRelatedWork W4205958290 @default.
• W2894844737 hasRelatedWork W4225312515 @default.
• W2894844737 hasRelatedWork W4246246790 @default.
• W2894844737 hasRelatedWork W4281846282 @default.
• W2894844737 hasRelatedWork W4293069612 @default.
• W2894844737 hasRelatedWork W4311106074 @default.
• W2894844737 hasVolume "7" @default.
• W2894844737 isParatext "false" @default.
• W2894844737 isRetracted "false" @default.

• next