https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 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 ]]> Risk factors identification of malignant mesothelioma: a data mining based approach https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:48655 Tue 14 Nov 2023 14:43:11 AEDT ]]>