Diagnosis of heart failure from imbalance datasets using multi-level classification

The incidence of heart failure is continuing to rise, and the mortality rate is high. Chest X-ray (CXR) has irreplaceable advantages in diagnosing heart failure, such as fast, low risk, and cheap. However, excessive CXR images place a huge burden on physicians and create data imbalance problems. Traditional methods, such as random under-sampling, are used to solve the problems. However, the under-sampling method can destroy the integrity of the data distribution. So, it is necessary to have a method that can address data imbalance problems and assist overburdened healthcare systems. This study establishes an automatic heart failure diagnosis system based on deep learning from imbalance datasets. To address the data imbalance problem based on the publicly available CheXpert dataset, this study proposes a method combining under-sampling and instance selection to ensure the integrity of the data distribution. To help physicians better treat heart failure, this study proposes an end-to-end multi-level classification method to diagnose the specific causes of heart failure. On the testing set, our method improves the average accuracy by 3.78% compared to the traditional random under-sampling method, and the accuracy of our end-to-end multi-class classification experiment is 84.44%. The heart failure automated diagnostic system is more efficient and accurate in diagnosing heart failure compared to state-of-the-art methods.