Computer Based Risk Analysis and Classification in Patients Suffering From Congestive Heart Failure

Abstract: Medical diagnosis is considered an art regardless of all standardization efforts made, which is greatly due to the fact that medical diagnosis necessitates an expertise in coping with uncertainty simply not found in today's computing machinery. The researchers are encouraged by the advancement in computer technology to develop software to assist doctors in making decision without necessitating the direct consultation with the specialists. Diagnosis of Heart disease is a important and critical task which can provide prediction about the heart disease so that treatment made easy. The main objective of this research is to develop a Disease Prediction Scheme using data mining modelling technique, namely, Naïve Bayes. Here, data mining played a vital role in diagnosis of heart disease with improved value. So, analyzing those diagnosis techniques may lead to new improvement in area of research. This is implemented as web based questionnaire .Based on the answers, it can discover and extract hidden knowledge (patterns and relationships) associated with heart disease from a historical heart disease databank. It can answer difficult queries for analysing heart disease and thus assist healthcare practitioners to make intelligent clinical decisions which traditional decision support systems cannot. By providing perfect treatments, it also supports to reduce medical treatment costs effectively.

Keywords: CHF-Congestive Heart Failure, Data Mining, ECG –Electro Cardio Graphic, HRV –Heart Rate Variability.