https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 A Machine Learning Approach for Identification of Malignant Mesothelioma Etiological Factors in an Imbalanced Dataset https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:49626 Wed 24 May 2023 13:25:52 AEST ]]> A novel method for performance measurement of public educational institutions using machine learning models https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:39585 Wed 07 Feb 2024 15:03:57 AEDT ]]> A novel framework for prognostic factors identification of malignant mesothelioma through association rule mining https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:46300 500 IU/L, C-reactive protein >10/μL, pleural albumin<3/μL, the presence of asbestos exposure and pleural effusion. In nearly all the experiments, the binary features were among the leading top five features in the list. The diagnosis of MM can be accessible through prognostic factors. Our proposed framework will help to diagnose the patients without expensive tests and painful procedures. The proposed framework may assist doctors, patients, medical practitioners, and other healthcare professionals for early diagnosis and better treatment of malignant mesothelioma through significant prognostic factors.]]> Tue 15 Nov 2022 10:10:30 AEDT ]]> An investigation of credit card default prediction in the imbalanced datasets https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:38537 Fri 29 Oct 2021 12:07:53 AEDT ]]>