TY - JOUR
T1 - Comprehensive machine learning models for prediction of heart failure in 476,393 women and men from the UK Biobank reveal sex differences and underutilized risk factors
AU - Kok, T. F.
AU - Suthahar, N.
AU - Krijthe, J.
AU - De Boer, R. A.
AU - Boersma, E.
AU - Kardys, I
PY - 2024/10/28
Y1 - 2024/10/28
N2 - ML models showed similar performance to Cox PH models for HF prediction. Despite this, differences in predictor importance were identified between models. Sex-specific risk predictors were found, and FEV1 score, which is not commonly included in existing models, was identified as an important risk factor. These results suggest that ML models may reveal additional insights that would otherwise remain unnoticed.
AB - ML models showed similar performance to Cox PH models for HF prediction. Despite this, differences in predictor importance were identified between models. Sex-specific risk predictors were found, and FEV1 score, which is not commonly included in existing models, was identified as an important risk factor. These results suggest that ML models may reveal additional insights that would otherwise remain unnoticed.
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=eur_pure&SrcAuth=WosAPI&KeyUT=WOS:001345393200044&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.1093/eurheartj/ehae666.1189
DO - 10.1093/eurheartj/ehae666.1189
M3 - Meeting Abstract
SN - 0195-668X
VL - 45
JO - European Heart Journal
JF - European Heart Journal
IS - Supplement 1
M1 - ehae6661189
T2 - European-Society-of-Cardiology Congress (ESC)
Y2 - 30 August 2024 through 2 September 2024
ER -