An accuracy study of the YouDiagnose disease predictive model based on a double-validated real-world dataset from the UK

Through the utilisation of algorithms and data-driven analytics, predictive technology can be leveraged to provide clinicians with invaluable insight into their patients conditions, allowing for more accurate, informed, and timely decision-making in the fast-paced clinical environment. This study aims to provide a preliminary proof of concept with a double-validated real-world dataset from the UK. The YouDiagnose predictive model, developed using retrospective data from over 41,257 patients data, was assessed by testing it with a double-validated real-world dataset from the UK and the machine predictions have been compared here with the final diagnosis of the diseases as the gold standard. Out of the total of 433 cases, 60 cases had a mismatch in their prediction, all of them being cancer overdiagnosis, resulting in a lower specificity rate of 84.3%. The combined prediction accuracy at the first prediction level was 86% (n=373) while prediction 1-3 combined was successful in predicting diseases in 93% of the cases when evaluated against the gold standard. The model accurately predicted all 52 cases of cancer, indicating a 100% sensitivity rate. The study shows that the tool can be used in the frontline to accurately screen patients with a high level of confidence in the inclusion of cancer patients. This tools high sensitivity means that there is little chance of missing any cancer cases.