Hybrid Machine Learning Classification Technique for Improve Accuracy of Heart Disease Prediction

Authors

  • M. Poojitha  Department of Computer Science and Engineering, Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, India
  • Dr. Srinivasanjagannathan  Associate Professor, Department of Computer Science and Engineering, Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, India

Keywords:

RESNET, VGG, Deep Learning, Facial sentiment, FER 2013.

Abstract

Human looks are an important way to convey emotions. In the field of PC vision, the programmed examination of these implicit opinions has been a fascinating and challenging endeavor with applications in a variety of fields, including brain research, product promotion, process robotization, and so on. This task has been hard because there are so many different ways people express their emotions through expression. Already, different strategies for AI, like Irregular timberland and SVM, were utilized to utilize changed pictures over completely to anticipate the opinion. In many areas of research, including PC vision, deep learning has been crucial to making progress. We use a model based on a convolutional neural network (CNN) to detect facial sentiment. For testing and training, the FER-2013 public dataset is utilized.

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Published

2023-08-30

Issue

Section

Research Articles

How to Cite

[1]
M. Poojitha, Dr. Srinivasanjagannathan, " Hybrid Machine Learning Classification Technique for Improve Accuracy of Heart Disease Prediction" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 4, pp.178-183, July-August-2023.