Abstract
Cardiovascular disease/heart disease is one of the chronic diseases prevailing across the world. Prediction of heart disease in an efficient and in a timely manner is difficult. The majority of the existing work for predicting heart disease focuses on machine learning techniques, but they failed to attain higher accuracy. Recent developments in deep learning techniques has significant impact on data analytics. So, the proposed work here combines convolutional neural networks with a long short term memory (LSTM) network to achieve higher accuracy than the traditional machine learning approaches. The hybrid CNN and LSTM method was applied over the heart disease dataset to classify it as normal and abnormal. This hybrid system has shown an accuracy of 89%, and it was validated using k-fold cross-validating technique. To establish the efficiency of proposed method, it is compared with various machine learning algorithms such as SVM, Naïve Bayes and Decision Tree. The results shows that the proposed algorithm achieves better performance than the existing machine learning models.
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This article is part of the topical collection “Advances in Computational Intelligence for Artificial Intelligence, Machine Learning, Internet of Things and Data Analytics” guest edited by S. Meenakshi Sundaram, Young Lee and Gururaj K S.
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Sudha, V.K., Kumar, D. Hybrid CNN and LSTM Network For Heart Disease Prediction. SN COMPUT. SCI. 4, 172 (2023). https://doi.org/10.1007/s42979-022-01598-9
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DOI: https://doi.org/10.1007/s42979-022-01598-9