Gulshan Tajuria, David Dobel-Ober, Eleanor Bradley, Claire Charnley, Ruth Lambley-Burke, Christian Mallen, Kate Honeyford, Tom Kingstone
Evaluating the impact of the supporting the advancement of research skills (STARS) programme on research knowledge, engagement and capacity-building in a health and social care organisation in England
BMC medical education
advent artificial intelligence technology machine learning algorithms widely used area disease prediction cardiovascular disease cvd seriously jeopardizes human health worldwide thereby needing establishment effective cvd prediction model great significance controlling risk disease safeguarding physical mental health population considering uci heart disease dataset example initially single machine learning prediction model constructed subsequently six methods pearson chisquared rfe lightgbm comprehensively used feature screening basis base classifiers soft voting fusion stacking fusion carried build prediction model cardiovascular diseases order realize early warning disease intervention highrisk populations address data imbalance problem smote method adopted process data set prediction effect model analyzed using multidimensional multiindicators single classifier model mlp algorithm performed optimally preprocessed heart disease dataset feature selection five features eliminated ensemsv algorithm combines base classifiers determine prediction results soft voting results classifiers achieved optimal value five metrics accuracy jaccardscore hammloss auc etc auc value reached 0951 rf et gbdt lgb algorithms employed first stage submodel composed base classifiers ab algorithm selected second stage model ensemble algorithm ensemst obtained stacking fusion two stages exhibited best performance 7 indicators accuracy sensitivity f1score mathewcorrcoef etc auc reached 0952 furthermore comparison algorithms classification effects based different training set occupancy carried results indicated prediction performance fusion models better single models overall effect ensemst fusion stronger ensemsv fusion fusion model established study improved overall classification accuracy stability model significant extent good application value predictive analysis cvd diagnosis provide valuable reference disease diagnosis intervention strategies