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](https://asset.jmir.pub/assets/00ee313c7599b06506ab97a575dc799c.png 480w,https://asset.jmir.pub/assets/00ee313c7599b06506ab97a575dc799c.png 960w,https://asset.jmir.pub/assets/00ee313c7599b06506ab97a575dc799c.png 1920w,https://asset.jmir.pub/assets/00ee313c7599b06506ab97a575dc799c.png 2500w)
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- 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
- 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
- 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