Deep Learning for Risky Cardiovascular and Cerebrovascular Event Prediction in Hypertensive Patients

被引:0
|
作者
Goretti, Francesco [1 ]
Salman, Ali [2 ]
Cartocci, Alessandra [3 ]
Luschi, Alessio [2 ]
Pecchia, Leandro [4 ]
Milli, Massimo [5 ]
Iadanza, Ernesto [2 ]
机构
[1] Univ Florence, European Lab Nonlinear Spect LENS, I-50019 Florence, Italy
[2] Univ Siena, Dept Med Biotechnol, I-53100 Siena, Italy
[3] Univ Siena, Dept Med Sci Surg & Neurosci, Dermatol Unit, I-53100 Siena, Italy
[4] Univ Campus Biomed, Biomed Engn Dept, I-00128 Rome, Italy
[5] Osped S Maria Nuova, USL Toscana Ctr, Dept Cardiol, I-50122 Florence, Italy
来源
APPLIED SCIENCES-BASEL | 2025年 / 15卷 / 03期
关键词
synthetic data generation; heart rate variability; atrial fibrillation; cardiovascular events; deep learning; transfer learning; event early detection; ATRIAL-FIBRILLATION; PREVALENCE; STROKE;
D O I
10.3390/app15031178
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
In this comprehensive study, we employed a versatile approach to tackle the prediction challenges associated with atrial fibrillation (AF) and cardiovascular events (CE). Exploiting the Gaussian copula synthesizer technique for data generation, we created high-quality synthetic data to overcome the limitations posed by scarce patient records. Heart rate variability (HRV), known to be an efficient indicator of cardiac health often used with artificial intelligence (AI), was used to train and optimize custom-built deep learning (DL) models. Additionally, we explored transfer learning (TL) to enhance the model capabilities by adapting our AF classification model to address CE classification challenges, effectively transferring learned features and patterns, without extensive retraining. As a result, our models achieved accuracy rates of 77% for AF and 82% for CEs, with high sensitivity, highlighting the efficacy of synthetic data generation and transfer learning in improving classification performance across diverse medical datasets. These findings hold significant promise for enhancing diagnostic and predictive capabilities in clinical settings, ultimately contributing to improved patient care and outcomes.
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页数:21
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