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Published in:
2022 | OriginalPaper | Chapter
Anticipation of Heart Disease Using Improved Optimization Techniques
Authors : Sibo Prasad Patro, Neelamadhab Padhy, Rahul Deo Sah
Published in: Computing, Communication and Learning
Publisher: Springer Nature Switzerland
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Abstract
Heart disease is one of the major cause that leads to a reduce the life span of human beings. Hence it is important to find abnormal heart conditions at an early stage that helps to avoid sudden cardiac death. The optimization algorithms are adaptable and flexible enough to handle difficult non-linear problems. A heart disease prediction model is proposed using Bayesian Optimization of Support Vector Machine, Salp Swarm Optimized Neural Network Classifier, Particle Swarm Optimization, Ant colony, Gradient Descent and Gradient Descent + Particle Swarm Optimization to identify the presence of heart disease. The Proposed GD+PSO mixed optimization algorithms produced the highest accuracy with 99.92% on Cleveland dataset. The proposed algorithms’ superiority is supported by numerous numerical, statistical, graphical, and comparative analyses involving numerous state-of-the-art algorithms. Finally, GD+PSO is suggested in this study for unconstrained optimization problems based on overall performance.