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<p>With the continuous development of digital technology and the continuous improvement of medical information databases, effective mining methods for potentially useful information behind medical data have become one of the research focuses of digital development in the medical field. In this paper, based on the deep forest model, a deep forest classifier framework based on the federated learning algorithm is constructed for the privacy protection of medical data and optimized by gradient boosting decision tree. Then the feature vector of cardiovascular diseases is constructed from engineering features, and feature selection is carried out through the constructed classifier algorithm to realize the prediction and diagnosis of epidemic diseases, followed by the experimental analysis of the method of this paper. The empirical analysis shows that the diagnostic accuracy of this paper’s model in seven common cardiovascular diseases is higher than 80%. Among them, the accuracy rate of heart valve disease is as high as 87%, and the diagnostic accuracy rate of arrhythmia and coronary heart disease is tied for second place with 83%. It shows that the predictive diagnosis model based on deep forest in this paper has good performance, can meet the actual needs of predictive diagnosis of cardiovascular diseases, and provides an effective reference for the development of auxiliary diagnosis in the digital era.</p>
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With the continuous development of digital technology and the continuous improvement of medical information databases, effective mining methods for potentially useful information behind medical data have become one of the research focuses of digital development in the medical field. In this paper, based on the deep forest model, a deep forest classifier framework based on the federated learning algorithm is constructed for the privacy protection of medical data and optimized by gradient boosting decision tree. Then the feature vector of cardiovascular diseases is constructed from engineering features, and feature selection is carried out through the constructed classifier algorithm to realize the prediction and diagnosis of epidemic diseases, followed by the experimental analysis of the method of this paper. The empirical analysis shows that the diagnostic accuracy of this paper’s model in seven common cardiovascular diseases is higher than 80%. Among them, the accuracy rate of heart valve disease is as high as 87%, and the diagnostic accuracy rate of arrhythmia and coronary heart disease is tied for second place with 83%. It shows that the predictive diagnosis model based on deep forest in this paper has good performance, can meet the actual needs of predictive diagnosis of cardiovascular diseases, and provides an effective reference for the development of auxiliary diagnosis in the digital era.

Deep Forest-based Disease Prediction and Diagnosis under the Concept of Digital Health

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the continuous development of digital technology and the continuous improvement of medical information databases, effective mining methods for potentially...

Deep Forest-based Disease Prediction and Diagnosis under the Concept of Digital Health

Publié en ligne: 05 juil. 2024

Pages: -

Reçu: 10 juin 2024

DOI: https://doi.org/10.2478/amns-2024-1625

Mots clés
Deep forest model, Federated learning, Engineering features, Decision tree, Cardiovascular disease diagnosis

© 2024 Xiangxiang Mei et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Deep Forest-based Disease Prediction and Diagnosis under the Concept of Digital Health

Publié en ligne: 05 juil. 2024

Pages: -

Reçu: 10 juin 2024

DOI: https://doi.org/10.2478/amns-2024-1625

Mots clésDeep forest model, Federated learning, Engineering features, Decision tree, Cardiovascular disease diagnosis

© 2024 Xiangxiang Mei et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Deep forest model, Federated learning, Engineering features, Decision tree, Cardiovascular disease diagnosis