Abstract
In this paper, we aim to predict accuracy, whether the individual is at risk of a heart disease. This prediction will be done by applying machine learning algorithms on training data that we provide. Once the person enters the information that is requested, the algorithm is applied and the result is generated. Obviously, the accuracy is expected to decrease when the medical data itself are incomplete. We implement the prediction model over real-life hospital data. We propose to use convolutional neural network algorithm as a disease risk prediction algorithm using structured and perhaps even on unstructured patient data. The accuracy obtained using the developed model ranges between 85 and 88%. We have proposed further by applying other machine learning algorithms over the training data to predict the risk of diseases, comparing their accuracies so that we can deduce the most accurate one. Attributes can also be modified in an attempt to improve the accuracy further.
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28 September 2023
A Correction to this paper has been published: https://doi.org/10.1007/s42979-023-02168-3
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Shankar, V., Kumar, V., Devagade, U. et al. Heart Disease Prediction Using CNN Algorithm. SN COMPUT. SCI. 1, 170 (2020). https://doi.org/10.1007/s42979-020-0097-6
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DOI: https://doi.org/10.1007/s42979-020-0097-6