Machine learning to identify multigland disease in primary hyperparathyroidism

The Journal of Surgical Research
Joseph R ImbusDavid F Schneider

Abstract

20%-25% of patients with primary hyperparathyroidism will have multigland disease (MGD). Preoperatative imaging can be inaccurate or unnecessary in MGD. Identification of MGD could direct the need for imaging and inform operative approach. The purpose of this study is to use machine learning (ML) methods to predict MGD. Retrospective review of a prospective database. The ML platform, Waikato Environment for Knowledge Analysis, was used, and we selected models for (1) overall accuracy and (2) preferential identification of MGD. A review of imaging studies was performed on a cohort predicted to have MGD. 2010 patients met inclusion criteria: 1532 patients had single adenoma (SA) (76%) and 478 had MGD (24%). After testing many algorithms, we selected two different models for potential integration as clinical decision-support tools. The best overall accuracy was achieved using a boosted tree classifier, RandomTree: 94.1% accuracy; 94.1% sensitivity, 83.8% specificity, 94.1% positive predictive value, and 0.984 area under the receiver operating characteristics curve. To maximize positive predictive value of MGD prediction, a rule-based classifier, JRip, with cost-sensitive learning was used and achieved 100% positive predictive valu...Continue Reading

Citations

Mar 10, 2020·F1000Research·Melanie Goldfarb, Frederick R Singer
Feb 14, 2018·Briefings in Bioinformatics·Igor F Tsigelny
Aug 11, 2020·Annals of Surgery·Susan C Pitt

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