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Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I

Research Article

Enhancing Heart Disease Prediction Through a Heterogeneous Ensemble DL Models

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-48888-7_5,
        author={J. N. S. S. Janardhana Naidu and Mudunuri Aniketh Varma and P. Shyamala Madhuri and D. Shankar and Durga Satish Matta and Singaraju Ramya},
        title={Enhancing Heart Disease Prediction Through a Heterogeneous Ensemble DL Models},
        proceedings={Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I},
        proceedings_a={IC4S},
        year={2024},
        month={1},
        keywords={Heart disease prediction Ensemble DL ML CNN RNN healthcare decision-making},
        doi={10.1007/978-3-031-48888-7_5}
    }
    
  • J. N. S. S. Janardhana Naidu
    Mudunuri Aniketh Varma
    P. Shyamala Madhuri
    D. Shankar
    Durga Satish Matta
    Singaraju Ramya
    Year: 2024
    Enhancing Heart Disease Prediction Through a Heterogeneous Ensemble DL Models
    IC4S
    Springer
    DOI: 10.1007/978-3-031-48888-7_5
J. N. S. S. Janardhana Naidu1,*, Mudunuri Aniketh Varma1, P. Shyamala Madhuri1, D. Shankar1, Durga Satish Matta1, Singaraju Ramya2
  • 1: Department of Computer Science and Engineering, Vishnu Institute of Technology, Bhimavaram
  • 2: Department of Computing Technologies
*Contact email: janardhana.j@vishnu.edu.in

Abstract

Accurate and highly effective forecasting approaches for timely detection and management are imperative given that cardiovascular illness is one of the biggest causes of death world wide. Machine learning (ML) and Deep learning (DL) methodologies have produced promising outcomes. In particular, ensemble DL models have gained attention for their ability in order to capitalize on the advantages of many models to enhance predictive performance. This study focuses on applying a cardiovascular disease prognosis with a DL ensemble. The model combines the predictions of multiple DL models, networks of neurons, such as supervised DL Models (CNN and RNN), to magnify accuracy and robustness. In this research, the effectiveness of an ensemble model is measured using an ample dataset, comparing it with individual DL models. The model’s prediction skills are evaluated by utilizing a verity of Evaluation metrics. The findings highlight the effectiveness of cardiovascular disease ensemble DL models prediction, showcasing their potential for enhancing diagnostic accuracy in clinical settings and aiding healthcare professionals in making informed decisions for patient care.

Keywords
Heart disease prediction Ensemble DL ML CNN RNN healthcare decision-making
Published
2024-01-05
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-48888-7_5
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