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A Dense Network Approach with Gaussian Optimizer for Cardiovascular Disease Prediction

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Abstract

The effective method for cardiovascular disease (CVD) risk prediction is done by training the deep neural networks on the well-defined training dataset. The irregular subset from the real dataset with a greater data variance is considered for prediction. The proposed system uses the trained datasets to separate common and greatly biased subsets for accurately implementing the prediction models when many previous models are learning from the random samples of training datasets. The feature selection is done with a Binary Krill Herd meta-heuristic optimizer (B-KHA), and the extracted features are fed to the CapNet model for prediction purposes. In addition, the isolated training groups learn the network classifiers. This proposed model used the Cleveland dataset gathered from online resources. The experiment proves that the proposed model improves the network performance by appropriate prediction. The suggested model shows that the experimental outcomes perform better than the traditional machine learning algorithms, with 95% accuracy, 94% specificity, 94% precision, 97% sensitivity, 95% F1-score, and 90% Mathews’ Correlation Coefficient (MCC).

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Data availability

The datasets generated and analysed during the current study are available in the UCI machine learning repository.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India

    A. Saran Kumar

  2. Department of Information Technology, PSG College of Technology, Coimbatore, Tamil Nadu, India

    R. Rekha

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  1. A. Saran Kumar
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  2. R. Rekha
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Correspondence to A. Saran Kumar.

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Kumar, A.S., Rekha, R. A Dense Network Approach with Gaussian Optimizer for Cardiovascular Disease Prediction. New Gener. Comput. 41, 859–878 (2023). https://doi.org/10.1007/s00354-023-00234-1

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  • DOI: https://doi.org/10.1007/s00354-023-00234-1

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