Heart illness prediction using hybrid machine learning techniques

Abstract

Diseases can influence individuals both genuinely and intellectually, as contracting and living with an
illness can adjust the influenced individual's viewpoint on life. A sickness that influences the parts of a living being,
which isn't in view of any quick outer injury. Diseases are regularly known to be ailments that are identified with explicit
indications and signs. The deadliest sicknesses in people are arteria coronaries disease (blood stream impediment),
trailed by cerebrovascular sickness and lower respiratory contaminations. Coronary diseases are most eccentric and
unexpectable. We can become ready to foresee the coronary illness utilizing AI strategy. The datasets are taken from
UCI store which is a public dataset. These prepared datasets are utilized for the expectation. Procedures like Decision
tree, Support Vector Machine, K Nearest Neighbor and Random Forest algorithms are utilized in the expectation of
coronary illness and cross breed of these algorithms gives 94 % precision.