This paper is in the following e-collection/theme issue:

Cardiac Risk and Cardiac Risk Calculators (47) Machine Learning (1501) Artificial Intelligence (1421) Coronary Heart Disease (81)

Published on in Vol 6, No 2 (2022): Jul-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38040, first published .
The Impact of Time Horizon on Classification Accuracy: Application of Machine Learning to Prediction of Incident Coronary Heart Disease

The Impact of Time Horizon on Classification Accuracy: Application of Machine Learning to Prediction of Incident Coronary Heart Disease

The Impact of Time Horizon on Classification Accuracy: Application of Machine Learning to Prediction of Incident Coronary Heart Disease

Authors of this article:

Steven Simon1 Author Orcid Image ;   Divneet Mandair2 Author Orcid Image ;   Abdel Albakri2 Author Orcid Image ;   Alison Fohner3 Author Orcid Image ;   Noah Simon4 Author Orcid Image ;   Leslie Lange5 Author Orcid Image ;   Mary Biggs6 Author Orcid Image ;   Kenneth Mukamal7 Author Orcid Image ;   Bruce Psaty3, 8 Author Orcid Image ;   Michael Rosenberg1 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Journals

  1. Yang H, Chen Z, Yang H, Tian M. Predicting Coronary Heart Disease Using an Improved LightGBM Model: Performance Analysis and Comparison. IEEE Access 2023;11:23366 View
  2. Tiruye T, Roder D, FitzGerald L, O’Callaghan M, Moretti K, Beckmann K. Utility of prescription-based comorbidity indices for predicting mortality among Australian men with prostate cancer. Cancer Epidemiology 2024;88:102516 View
  3. Du J, Yang J, Yang Q, Zhang X, Yuan L, Fu B. Comparison of machine learning models to predict the risk of breast cancer-related lymphedema among breast cancer survivors: a cross-sectional study in China. Frontiers in Oncology 2024;14 View