In a study conducted by Li et al., the aim was to develop and validate machine learning (ML) models for predicting sudden cardiac death (SCD) risk. Data from over 30,000 patients aged 65 and above with around six years of follow-up appointments were collected from the Jinnan Study in China, which used electronic health records (EHRs). Using this data, the researchers compared multiple ML models, including Cox regression, Fine-Gray, and random survival forests (RSF), all to identify key predictors of SCD. Candidate predictors included gender, age, heart rate, body mass index (BMI), blood pressure, physical activity, current smoking status, diabetes, and various laboratory and ECG measures. By using the key predictors and evaluating 10 models, the researchers found that the RSF-based model provided superior discrimination and calibration performance as it achieved a C-index of 0.82 in training and 0.76 in external validation. Furthermore, they also determined what factors are most important in predicting SCD risk. They found that the occurrence of SCD in males was two to three times higher than in females; additionally, the steepest SCD increase occurred in the elderly population. They also found that increased levels of physical activity, not smoking, and appropriate BMI were associated with reduced cardiovascular disease risk. They argued that the combination of understanding patient EHRs and using the proposed RSF-based model may be used to identify SCD high-risk in the older adults of the community.