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dc.contributor.authorChin, Pei Loon.-
dc.description.abstractEarly detection of ovarian cancer is critical to allow women to seek treatment when the cancer is still in the early stages, which will increase their chances of survival. Unfortunately, detection tools available now are either too expensive or they do not have enough sensitivity and specificity. The difficulty of finding a good detection tool is further increased by the presence of unavoidable missing data. Currently there are no suitable method of handling missing data which can be employed easily while maintaining the accuracy and sample size. The derivation of a good missing data handling method will allow experiments to proceed smoothly with an increased precision and reliability of the final cancer detection result. 7 different classifiers were tested out in this project for their accuracy in classifying 145 samples of ovarian cancer. As ensemble learning has the capability of reducing the likelihood or a poor selection, results of different models within each classifier were combined for ensemble learning. Ensemble learning demonstrated its strength by improving accuracy by nearly 7%.en_US
dc.format.extent80 p.en_US
dc.rightsNanyang Technological University-
dc.subjectDRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciencesen_US
dc.titleA novel fuzzy neural ensemble decision support system for whisper cancer analysisen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorChan Syinen_US
dc.contributor.supervisorQuek Hiok Chaien_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.researchCentre for Computational Intelligenceen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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Updated on Jan 26, 2021


Updated on Jan 26, 2021

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