Predictive Classification for Cardiovascular Disease Diagnosis Using Ensemble Recurrent Neural Network

Ashok Ramaswami; N. Kanya; T.  Ravi; G.  Victo Sudha George

doi:10.55938/ijgasr.v5i1.305

Authors

Ashok Ramaswami Dr. M.G.R. Educational and Research Institute
N. Kanya Dr. M.G.R. Educational and Research Institute
T. Ravi Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
G. Victo Sudha George Dr. M.G.R. Educational and Research Institute

DOI:

https://doi.org/10.55938/ijgasr.v5i1.305

Keywords:

Predictive Classification, Cardiovascular Disease Diagnosis, Ensemble Learning, Recurrent Neural Network, Health Records

Abstract

Health significance depends much on correct and quick diagnosis of cardiovascular illnesses (CVDs). Many times, lacking the accuracy required for early diagnosis, conventional methods of diagnosis Advanced prediction models so help one another in the diagnosis of CVD by way of complementing each other. An Ensemble Recurrent Neural Network (ERNN) based fresh method for CVD diagnosis is presented in this paper. ERNN may categorize things more accurately by integrating the prediction powers of several different recurrent neural network topologies. The model's training data set includes medical records and risk factors for heart disease. The proposed system can give more consistent patient results by using ensemble learning methods, which make the system more resilient and increase the accuracy of diagnoses. Researchers have shown that a classification system based on ERNN works well with a large dataset. The model is better than traditional diagnostic tools and methods that just employ one model because it is more sensitive and accurate when it comes to predicting the activity of CVD.

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