New Improved Genetic Algorithm for Coronary Heart Disease Prediction

Abstract

Heart disease prediction is treated as most complicated task in the field of medical sciences. Thus there arises a need to build a decision support system for detecting heart disease of a patient. Almost all system predicting heart disease use inputs from complex tests conducted in labs. In this project we are developing a system which will predict heart based on the risk factors such as tobacco, smoking, alcohol intake, age, family history, diabetes, hypertension, high cholesterol, physical inactivity, obesity. These common risk factors can be used effectively for diagnosis of heart disease [1]. System based on the such risk factors would not only help medical professionals but it would give patients a warning about the probable presence of the heart disease even before he/she visits a hospital or goes for costly medical checkups.

Comparative Analysis of Software Components Reusability Level using GFS and ANFIS Soft-Computing Techniques

“…Genetic fuzzy system is a system that exploits genetic algorithms to automatically generate or optimise the knowledge base of a fuzzy system; since the fuzzy system is not able to learn on its own. Researches have shown that hybridised genetic algorithm gives a more accurate predictive result [17][18] [19].…”