Identification of Heart Failure by Using Unstructured Data of Cardiac Patients

摘要

Heart Failure (HF) occurrence is increasing day by day and is the leading death cause disease in our society. HF is among the most expensive diseases as well. Social and individual burden of this disease can be reduced by early detection of HF. This would provide the means that may helpful to slow progression of the disease as well as to recover patient to good health. In this research study, we have applied data mining techniques to get useful information from medical reports of patients and using machine learning classification algorithm, we propose a risk model to predict 1-year or more survival for HF diagnosed patients. To perform multi-class classification we use multi-nominal Naïve Bayes (NB) classification algorithm. We got our required data from the Armed Forces Institute of Cardiology (AFIC), Pakistan, in the form of medical reports of patients which are available in the structured and unstructured format. Unfortunately, a lot of information is buried in unstructured data format. Our proposed model achieved an accuracy and Area under the Curve (AUC) of 86.7% and 92.4%, respectively.