Multi-Label Chest X-Ray Classification - CheXpert Data

Our goal in this paper is to develop a lightweight solution to detect 14 different chest conditions from an X ray image. Given an X-ray image as input, our classifier outputs a label vector indicating which of 14 disease classes does the image fall into. For training, we used dataset consisting of 224,316 chest radiographs of 65,240 patients who underwent a radiographic examination from Stanford University Medical Center between October 2002 and July 2017.

Heart Disease Classification using Tabnet

 This research aims to develop an artificial intelligence-based system that identifies patients who are more likely to develop heart disease based on their medical history. The heart disease dataset from the UCI Machine Learning Repository was used for training and validation.