Maryam Bakhshandeh Bavarsad, Mahshid Foroughan, Nasibeh Zanjari, Gholamreza Ghaedamini Harouni, Zahra Jorjoran Shushtari
Development and validation of the geriatrics health behavior questionnaire (GHBQ)
BMC public health
adrenal gland incidentaloma frequently identified computed tomography poses common clinical challenge selected cases require surgical intervention primary aim study compare effectiveness selected machine learning ml techniques proper qualifying patients adrenalectomy identify accurate algorithm providing valuable tool doctors simplify therapeutic decisions secondary aim assess significance attributes classification accuracy total clinical data collected 33 patients underwent adrenalectomy histopathological assessments confirmed proper selection 21 patients surgical intervention according guidelines accuracy reaching 64 statistical analysis showed supported vector machines linear significantly better baseline p 005 accuracy reaching 91 imaging features tumour found crucial attributes summarise ml methods may helpful qualifying patients adrenalectomy