Abstract
A recent study shows that almost 30% of total global deaths are caused by
heart disease. These days precise diagnosis related to heart disease is very difficult. The
doctor advises patients to take various tests for diagnosis, which is a very costly and
time-consuming process as medical databases are large and cannot be processed
quickly. A new approach has been proposed to predict heart disease from historical
data sets. In this chapter, heart disease possibilities in patients are predicted with the
help of neural networks on distributed computing. Feature selection was applied to the
dataset to get better results and to increase the performance. Feature selection reduces
the number of attributes from the dataset and only provides the necessary attributes,
which directly reduces the number of tests required for the diagnosis.
Related Journals
Recent Patents on Computer Science
The Chinese Journal of Artificial Intelligence
Current Chinese Computer Science
Journal of Fuzzy Logic and Modeling in Engineering
Recent Advances in Computer Science and Communications
International Journal of Sensors, Wireless Communications and Control
Journal of Intelligent Systems in Current Computer Engineering
Current E-Learning
Current Computer Science
Wireless and Communication Letters
Related Books
Computational Intelligence For Data Analysis
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
A First Course in Artificial Intelligence
Artificial Intelligence: Models, Algorithms and Applications
Advanced Computing Techniques: Implementation, Informatics and Emerging Technologies
Handbook of Mobile Application Development: A Guide to Selecting the Right Engineering and Quality Features
Applications of Modern High Performance Networks
Arduino Meets Matlab: Interfacing, Programs and Simulink
Arduino and SCILAB based Projects
Application of Chaos and Fractals to Computer Vision