Heart Disease Prediction using Data Mining

Abstract

Heart disease prediction by means of data mining is one of the most interesting and demanding chore. The
shortage of specialists and high wrongly diagnosed cases has necessitated the need to develop a fast and efficient detection
system. Many hospital information systems are intended to carry patient billing, record managing and generation of simple
statistics. Some hospitals use decision support systems, but they are largely limited. They can answer simple queries like
“What is the average age of patients who have heart disease?”, “How many surgeries had resulted in hospital stays longer
than 10 days?”, “Identify the female patients who are single, above 30 years old, and who have been treated for cancer.”
However, they cannot answer complex queries like “Identify the important preoperative predictors that increase the length of
hospital stay”, “Given patient records, predict the probability of patients getting a heart disease.”
In this system first we analyze the history data of patient and by getting risky factor disease is predicted using support vector
machine.