{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T19:45:01Z","timestamp":1790279101881,"version":"4.1.0"},"reference-count":39,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T00:00:00Z","timestamp":1757462400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>In order to seriously impact the global burden of heart failure (HF) and coronary artery disease (CAD), identifying at-risk individuals as early as possible is vital. Risk calculator tools in wide clinical use today are informed by traditional statistical methods that have historically yielded only modest prediction accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study uses machine learning algorithms to generate predictions models for the development and progression of severe HF and CAD. Participants (~485,000 followed in the UK Biobank over 7 years) were stratified by cardiac status at the time of enrollment (asymptomatic, high-risk and affected); separate prediction models were built for each stratum. Participants were split between a training set (80%) and holdout dataset (20%), all performance metrics are reported for the holdout dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Out of 6 machine learning algorithms screened, artificial neural networks (ANN) most successfully predicted future disease across the various strata (area under the curve: 0.77\u20130.86 for 10\/12 models), results were very consistent between methodologies. Models trained using ANN showed excellent calibration in all strata and across the entire spectrum of risk (0.4\u20131.2% average observed\/predicted difference across 10 deciles of risk). Key predictive features included age, frailty, adiposity, history of hypertension and diabetes, tobacco use and family history of heart disease and were consistent between models for HF and CAD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>When deployed as a patient-facing application, the prediction models presented here will be able to provide both user-specific predictions and simulate the effect of changes in lifestyle and of prophylaxis interventions, thus resulting in an individualized patient counselling and management tool.<\/jats:p>\n                  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