Risk factor refinement and ensemble deep learning methods on prediction of heart failure using real healthcare records

Zhou, Chunjie; Hou, Aihua; Dai, Pengfei; Li, Ali; Zhang, Zhenxing; Mu, Yuejun; Liu, li

doi:10.1016/j.ins.2023.04.011

Cited by 15 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, early detection of heart failure is essential since it gives researchers the chance to test and create efficient pharmaceutical and lifestyle therapies. This is particularly significant due to the fact it may help prevent or delay the progression of heart illnesses, lowering the risk of mortality [5]. A subset of machine learning (ML) known as "deep learning" (DL) often takes into account multiple layers of information-processing stages in hierarchical structures.…”

Section: Introductionmentioning

confidence: 99%

A Robust Heart Disease Prediction System Using Hybrid Deep Neural Networks

Reshan,

Amin,

Zeb

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

A Robust Heart Disease Prediction System Using Hybrid Deep Neural Networks

Reshan,

Amin,

Zeb

et al. 2023

IEEE Access

View full text Add to dashboard Cite

3D LVCN: A Lightweight Volumetric ConvNet

Lu,

Zhou,

Liu

et al. 2024

Concurrency and Computation

View full text Add to dashboard Cite

In recent years, with the significant increase in the volume of three‐dimensional medical image data, three‐dimensional medical models have emerged. However, existing methods often require a large number of model parameters to deal with complex medical datasets, leading to high model complexity and significant consumption of computational resources. In order to address these issues, this paper proposes a 3D Lightweight Volume Convolutional Neural Network (3D LVCN), aiming to achieve efficient and accurate volume segmentation. This network architecture combines the design principles of convolutional neural network modules and hierarchical transformers, using large convolutional kernels as the basic framework for feature extraction, while introducing 1 × 1 × 1 convolutional kernels for deep convolution. This improvement not only enhances the computational efficiency of the model but also improves its generalization ability. The pro‐posed model is tested on three challenging public datasets, namely spleen, liver, and lung, from the medical segmentation decathlon. Experimental results show that the proposed model performance has in‐creased from 0.8315 to 0.8673, with a reduction in parameters of approximately 5%. This indicates that compared to currently advanced model structures, our proposed model architecture exhibits significant advantages in segmentation performance.

show abstract

A comprehensive review of deep learning-based models for heart disease prediction

Zhou,

Dai,

Hou

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

Heart disease (HD) is one of the leading causes of death in humans, posing a heavy burden on society, families, and patients. Real-time prediction of HD can reduce mortality rates and is crucial for timely intervention and treatment of HD. Deep learning (DL)related methods have higher accuracy and real-time performance in predicting HD. In this study, we comprehensively compared and evaluated the contributions and limitations of DL algorithms, extended deep learning (ETDL) algorithms, and integrated deep learning (integrated DL) algorithms that combine DL with other technologies for predicting HD. The articles considered span the period from 2018 to 2023, and after rigorous screening, 64 articles were selected for preliminary research. A systematic literature review of realtime HDP will provide future researchers with a comprehensive understanding of existing deep learning methods and related integrated technologies in the healthcare industry. Furthermore, it discusses popular datasets employed in deploying numerous prediction models. Additionally, it reveals existing open problems or challenges encountered by previous researchers. Notably, the most prevalent challenge is the scarcity of large discrete datasets, followed by the need for further improvement of existing models.

show abstract

Risk factor refinement and ensemble deep learning methods on prediction of heart failure using real healthcare records

Cited by 15 publications

References 32 publications

A Robust Heart Disease Prediction System Using Hybrid Deep Neural Networks

A Robust Heart Disease Prediction System Using Hybrid Deep Neural Networks

3D LVCN: A Lightweight Volumetric ConvNet

A comprehensive review of deep learning-based models for heart disease prediction

Contact Info

Product

Resources

About