AN EFFECTIVE TECHNIQUE ON EARLY HEART DISEASE PREDICTION
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Date
2020-12-10
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Abstract
Heart disease is the most threatened issue in a human body. Most of the people in the world
are affected by heart disease which increases the death rate of humans considerably. The
detection of heart disease is the most trivial task for medical researchers which cannot be done
more accurately. The early detection, manual prediction and prevention is a complex task and
troublesome. So there is an urgent need for a well-designed method for the early detection of
heart disease. Thus it is required to implement the automated system by considering the recent
computer technologies which can help medical researchers to diagnosis heart disease firstly
and accurately. Now a day’s artificial neural network has been widely used as a tool for solving
many decision modelling problems. This research enlightened a number of structures in
Artificial Neural Network by varying the configurations of neural network algorithms like
Radial Basis Function Neural Network, Recurrent Neural Network, Decision tree classifier and
Support Vector Machine Neural Network. This research also compared those Neural Network
models on the early detection of heart disease and chose the best neural network model among
them to detect the heart disease early. The Network Structure that showed more accuracy and
efficiency is chosen among them.