BAGGING ENSEMBLE MINING TECHNIQUE WITH DEEP BELIEF NETWORK (DBN) ALGORITHM-BASED HEART DISEASE PREDICTION
Abstract
Cardiovascular disease is the most important disease of the heart, and the stage of the disease is diagnosed, the disease can be diagnosed anytime. The following method is used to find out its status. The heart disease prediction is based on Bagging Ensemble Technique with Deep Belief Network (DBN) algorithms. The problem for heart disease prediction from the collection of the dataset. Feature extraction using the Bag of Words methods to the correct data and discontinuities collect the heart disease-based matching data's extraction from the dataset and the various combination of the data set is available in Kaggle. It is mostly used for text classification methods. The proposed system of data mining is the most important technique for data aggregation. Data mining has various methods available, and one of the techniques is the bagging Ensemble Technique. In this method using for homogeneous data are quickly collected and parallel processing for the data collections. The first process for collecting data using the bagging Ensemble Technique is based on collected preprocessing. The Cardiovascular Disease prediction using Deep Belief Network (DBN) algorithm is compared with the existing system heart disease prediction, to prediction for display the data past and present movement in classification time and prediction accuracy, sensitivity and specificity. The proposed system for classification is compared with various techniques, and the proposed methods are DBN algorithms compared to compare to 5000 data'. The performance of CNN is 89%, RNN is 90%, LSTMs is 92%, and DBM is 95.6%. Finally, the heart disease prediction or classifications are given by the DBN algorithm.

Authors
M. Revathy Meenal, S. Mary Vennila
Presidency College, India

Keywords
Cardiovascular Disease Prediction, Bagging Ensemble Technique, Data Aggregation, Discontinuities in Brightness, Deep Belief Network (DBN) Algorithms
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Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 14 , Issue: 4 , Pages: 3293 - 3300 )
Date of Publication :
May 2024
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47
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