Cost-Saving Data-Driven Diabetic Retinopathy Prediction via a Sampling-Empowered Incremental Learning Approach

Diabetic retinopathy (DR) is a serious complication of diabetes that can lead to vision impairment or even blindness if not detected and treated in the early stage. Recently, leveraging the electronic health records (EHR) data, machine learning-based DR prediction becomes a promising research direction to achieve timely diagnosis of DR. In practice, the EHR database usually increases periodically, leading to an urgent need for an approach to update the DR prediction model by incorporating the new data. However, it is costly to keep retraining the model using combined data. Therefore, this study proposes to establish an effective incremental learning framework that allows the machine learning-based DR prediction model to continuously learn from new data while retaining knowledge from previous observations. Specifically, the proposed incremental learning approach integrates a weighted sampling strategy, so that the model is able to learn new information without forgetting previously learned patterns. The proposed sampling-empowered incremental learning approach was tested on different classification models. The results demonstrated that the proposed incremental learning framework with sampling strategy enables higher efficiency and even more accurate prediction of DR, while mitigating the challenges associated with periodically updated EHR database. By leveraging this approach, healthcare providers can achieve significant cost savings and maintain DR prediction accuracy.