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Heart disease classification using hybridized Ruzzo-Tompa memetic based deep trained Neocognitron neural network
Published in Springer Science and Business Media LLC
Volume: 10
Issue: 1
Pages: 207 - 216
According to the survey 17.5 million deaths are happened due to the cardiovascular disease that leads to create heart attack, chest pain and stroke. Based on the survey it clearly concludes that most of the people affected by heart problem that need to be identified in the earlier stage for eliminating the future risk in patient health. The importance of the heart disease detection process helps to create the earlier detection system for identifying heart problem by using machine learning and optimized techniques but the developed forecasting systems are difficult to predict the heart problems in an accurate manner with minimum time. So, hybridized Ruzzo–Tompa memetic based deep trained Neocognitron neural network is introduced to analyze the heart disease related features and predict the heart disease in earlier stage. First, heart disease data has been collected from UCI repository, dimensionality of the data is minimized by hybridized Ruzzo–Tompa memetic approach. After reducing the number of features, that are trained by deep learning approach which analyze the features using maximum number of hidden layers that used to predict heart disease features successfully while making the Neocognitron neural network classification. Further efficiency of the system is evaluated using MATLAB based simulation results. © 2019, IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature.
About the journal
JournalData powered by TypesetHealth and Technology
PublisherData powered by TypesetSpringer Science and Business Media LLC
Open AccessNo