Title :
Case-based learning for prediction of post-myocardial infarction outcomes
Author :
Dighe, S. ; Arunkumar, S. ; Gadkari, M.
Author_Institution :
Sch. of Biomed. Eng., Indian Inst. of Technol., Bombay, India
Abstract :
It is of prime importance to ascertain the prognosis of patients who have experienced heart attack or unstable angina, as they are prone to developing serious adverse after-effects. The aim of this study was to run machine learning algorithms over a cardiac database to derive associations between various clinical and pathological parameters and the occurrence of future adverse consequences. The rules induced from the data were used to build an expert system for prediction of outcomes for unseen cases and it was ported on the web for use over the Internet
Keywords :
Internet; cardiology; learning (artificial intelligence); medical expert systems; muscle; Internet medical application; cardiac database; case-based learning; clinical parameters; future adverse consequences; heart attack; machine learning algorithms; pathological parameters; postmyocardial infarction outcomes prediction; serious adverse aftereffects; unstable angina; Ambient intelligence; Biomedical engineering; Cardiac arrest; Computer science; Databases; Machine learning; Machine learning algorithms; Myocardium; Pathology; Web server;
Conference_Titel :
[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-5674-8
DOI :
10.1109/IEMBS.1999.802385
