ArticleBMJ health & care informatics2025
Machine learning prediction of germline
Article in BMJ health & care informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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12 authors.
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Abstract
objectivesTo assess the performance of machine learning (ML) algorithms to predict the presence of germline
methodsClinical-pathological features of 648 patients with OC tested for
resultsIn the 'test' sample, boosting proved to be the most effective algorithm (accuracy: 84.5%; precision: 80.0%; recall: 3.1%; area under the curve (AUC): 78.8%), followed by support vector machine (accuracy: 81.4%; precision: 72.7%; recall: 27.6%; AUC: 62.3%) and random forest (accuracy: 74.4%; precision: 55.6%; recall: 14.7%; AUC: 71.3%). In the 'validation' sample, accuracy was 79.8% for boosting, 81.7% for support vector machine, 80.8% for random forest.In the most effective algorithm (boosting), family history of OC showed the highest relative influence (52.9), followed by histotype (19.5), personal history of breast cancer (BC) (17.1), age at diagnosis (8.4) and family history of BC (2.2), while Federation of Gynecology and Obstetrics stage had no influence. DISCUSSION: We identified the predictive algorithm that best estimates the a priori likelihood of being a carrier of germline
conclusionsResults support the selection of relevant clinical features for predictive purposes, which could have significant implications for the clinical management of patients with OC.
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