Introduction Recent studies have reported that HbA1c and lipid variability is useful for risk
stratification in diabetes mellitus. The present study evaluated the predictive value of the …

Rationale and objectives To investigate the impact of random survival forest (RSF) classifier
trained by radiomics features over the prediction of the overall survival of patients with …

Identification of different risk factors and early prediction of mortality for patients with heart
failure are crucial for guiding clinical decision-making in Intensive care unit cohorts. In this …

A weighted random survival forest is presented in the paper. It can be regarded as a
modification of the random forest improving its performance. The main idea underlying the …

Heart disease remains the leading cause of death in the United States. Compared with risk
assessment guidelines that require manual calculation of scores, machine learning-based …

Arterial stiffness is strongly associated with cardiovascular events. Existing devices for
evaluating arterial stiffness based on ultrasound or pulse wave velocity suffer a lot from …

Aim Our study aimed to construct a practical risk prediction model for metabolic syndrome
(MetS) based on the longitudinal health check-up data, considering both the baseline level …

Rationale & Objective Chronic kidney disease (CKD) is a major cause of morbidity and
mortality. To date, there are no widely used machine-learning models that can predict …

The aim of our study was to assess the overall survival rates for colorectal cancer at 3 years
and to identify associated strong prognostic factors among patients in Morocco through an …

Objective To establish a model for predicting the disease-specific survival (DSS) of patients
with oral squamous cell carcinoma (OSCC). Methods Patients diagnosed with OSCC from …