This study recruited 500,000 participants, aged 40-69 years. Two datasets were used in this study. The first dataset consisted of obtaining  participants’ health measurements (smoking status, resting blood pressure). In addition, participants underwent paired retinal fundus and optical coherence tomography for fundus images. This dataset was divided into a development dataset to develop models and a validation dataset to assess the models’ performance. The second dataset-EyePACS-consisted of acquiring retinal fundus images from clinics’ routine clinical care for diabetic retinopathy screening. The images from both datasets were pre-processed for training and validation. Three separate neural network models were trained for predicting different risk factors: classification, regression, and MACE models. The performance of the neural network models was then assessed. First, MAE, the coefficient of determination, AUC, and Cohen’s k were used to evaluate the models’ performance for continuous predictions. The non-parametric bootstrap procedure and one-tailed binomial test were used to assess the statistical significance of the results. Finally, a deep-learning technique called soft attention (Fig. 2) was used to better understand how the neural-network models arrived at the predictions.