Hybrid model of convolutional neural network and long short term memory for heart disease prediction

Shubham Gupta, Pooja Sharma


Data mining is a process that assists in uncovering meaningful data from large, disorganized datasets. This research is being conducted to predict heart disorders by using available data to make predictions for the future. The approach is carried out in several stages, such as pre-processing the data, extracting relevant features, and classifying the data. all of these steps are essential for predicting heart disease. The deep learning models is already proposed by the researches for the heart disease prediction. This work introduces a hybrid deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) to predict heart disease. The proposed model has been implemented in python, and its accuracy, precision, and recall have been evaluated.


Convolutional neural network; Deep learning; Feature engineering; Heart disease; Long short-term memory

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DOI: http://doi.org/10.11591/ijeecs.v33.i1.pp389-397


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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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