Towards Predicting Risk of Coronary Artery Disease from Semi-Structured Dataset.

Many kinds of disease-related data are now available and researchers are constantly attempting to mine useful information out of these. Medical data are not always homogeneous and in structured form, and mostly they are time-stamped data. Thus, special care is required to prevent any kind of information loss during mining such data. Mining medical data is challenging as predicting the non-accurate result is often not acceptable in this domain. In this paper, we have analyzed a partially annotated coronary artery disease (CAD) dataset which was originally in a semi-structured form. We have created a set of some well-defined features from the dataset, and then build predictive models for CAD risk identification using different supervised learning algorithms. We then further enhanced the performances of the models using a feature selection technique. Experiments show that results are quite interesting, and are expected to help medical practitioners for investigating CAD risk in patients.