Lum Kastrati, Pedro Marques Vidal, Klodian Dhana, Lia Bally, Irene Lambrinoudaki, Dion Groothof, Stephan J L Bakker, Michele F Eisenga, Taulant Muka
Development and external validation of a home-based risk predicTion modEl of natUral onseT of menopAuse -TEUTA
The Journal of clinical endocrinology and metabolism
accurately classifying malignancy lesions detected screening scan critical reducing false positives radiomics holds great potential differentiate malignant benign tumors extracting analyzing large number quantitative image features since radiomic features contribute effective classifying model selecting optimal feature subset critical work proposes new multiobjective based feature selection mofs algorithm considers sensitivity specificity simultaneously objective functions feature selection mofs developed modified entropybased termination criterion stops algorithm automatically rather relying preset number generations also designed solution selection methodology multiobjective learning uses evidential reasoning approach smoler automatically select optimal solution paretooptimal set furthermore developed adaptive mutation operation generate mutation probability mofs automatically evaluated mofs classifying lung nodule malignancy lowdose ct breast lesion malignancy digital breast tomosynthesis experimental results demonstrated feature set selected mofs achieved better classification performance features selected commonly used methods proposed method general effective radiomic feature selection strategy