To create an algorithm assessing the risk of ovarian cancer in primary care using generalized additive 2 model (GA 2 M) and traditional methods
We aimed to evaluate models designed to support the exploration and early detection of potential Ovarian Cancer (OC) using either Machine Learning (ML) techniques or traditional methodologies, using primary care data. This evaluation aimed to facilitate appropriate and timely referrals to specialists. The Health Search database, containing healthcare records of 1 million adults, was used to predict OC cases among patients aged 18+ without prior OC diagnosis from 1 January 2002, to June 2021. GA Comparing the predictive performances of the three models, the AUC and AP for GA The GA