Prediction of mortality risk of health checkup participants using machine learning-based models: the J-SHC study

[1]  G. Tsakos,et al.  Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis. , 2021, Social science & medicine.

[2]  Arcot Sowmya,et al.  A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction , 2020, Scientific Reports.

[3]  D. Ashcroft,et al.  Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar , 2020, BMJ.

[4]  S. Muro,et al.  Machine Learning Methods for the Diagnosis of Chronic Obstructive Pulmonary Disease in Healthy Subjects: Retrospective Observational Cohort Study , 2020, JMIR medical informatics.

[5]  Association between serum uric acid levels and mortality: a nationwide community-based cohort study , 2020, Scientific Reports.

[6]  Hugh Chen,et al.  From local explanations to global understanding with explainable AI for trees , 2020, Nature Machine Intelligence.

[7]  J. Kai,et al.  Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches , 2019, PloS one.

[8]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[9]  J. Kai,et al.  Can machine-learning improve cardiovascular risk prediction using routine clinical data? , 2017, PloS one.

[10]  Dinesh Kant Kumar,et al.  Development of Health Parameter Model for Risk Prediction of CVD Using SVM , 2016, Comput. Math. Methods Medicine.

[11]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.