Clinical Synthetic Data Generation to Predict and Identify Risk Factors for Cardiovascular Diseases

García-Vicente, Clara; Chushig-Muzo, David; Jiménez, Inmaculada; Fabelo, Himar; Gram, Inger Torhild; Løchen, Maja‐Lisa; Granja, Conceição; Soguero-Ruíz, Cristina

doi:10.1007/978-3-031-23905-2_6

Cited by 7 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the inclusion of non-traditional risk factors like mental health markers has been explored to enhance predictive models [9]. Interestingly, while ML methods are at the forefront of predictive techniques, challenges such as class imbalance in datasets have been noted, which can be mitigated through oversampling [10]. Moreover, the integration of genetic information, specifically polygenic risk scores, is gaining attention for its potential to refine CVD risk prediction [11].…”

Section: Article Infomentioning

confidence: 99%

Untitled

2024

Card. Cardiovasc. Res.

View full text Add to dashboard Cite

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, accounting for a significant proportion of healthcare costs. It is a major public health concern, and early detection and prevention are critical for reducing its burden. Several risk factors for CVD have been identified, including age, sex, genetics, hypertension, diabetes, smoking, and physical inactivity. Despite the identification of several risk factors, there is still a lack of understanding of the underlying mechanisms that contribute to CVD development. This study aimed to investigate the potential predictors of CVD and identify novel biomarkers that could be used for early detection and prevention. The study explored pre-processing strategies, including Synthetic Minority Oversampling Technique (SMOTE), Z-Score Normalization, and Adaptive Synthetic Sampling (ADASYN), to address class imbalance and enhance model performance. The dataset consisted of medical images labeled with different cardiovascular diseases. By integrating the strengths of Support Vector Machines (SVM) classification and Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), and PCA with ReliefF feature retrieval methods, the study investigated various feature extraction approaches for classifying cardiovascular diseases.

show abstract

Section: Article Infomentioning

confidence: 99%

Untitled

2024

Card. Cardiovasc. Res.

View full text Add to dashboard Cite

show abstract

“…To date, different research steps have been carried out within WARIFA (Textbox 10), which include extensive literature reviews, risk factor mapping, data source and data protocol definitions, front-end development through a cocreation approach, and the development of preliminary AI-based algorithms. In addition, more details can be found in the project deliverables published so far [132] and related scientific publications [133][134][135].…”

Section: Core Ethical Principlesmentioning

confidence: 99%

Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges

Deniz-Garcia¹,

Fabelo²,

Rodriguez-Almeida³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

The use of artificial intelligence (AI) and big data in medicine has increased in recent years. Indeed, the use of AI in mobile health (mHealth) apps could considerably assist both individuals and health care professionals in the prevention and management of chronic diseases, in a person-centered manner. Nonetheless, there are several challenges that must be overcome to provide high-quality, usable, and effective mHealth apps. Here, we review the rationale and guidelines for the implementation of mHealth apps and the challenges regarding quality, usability, and user engagement and behavior change, with a special focus on the prevention and management of noncommunicable diseases. We suggest that a cocreation-based framework is the best method to address these challenges. Finally, we describe the current and future roles of AI in improving personalized medicine and provide recommendations for developing AI-based mHealth apps. We conclude that the implementation of AI and mHealth apps for routine clinical practice and remote health care will not be feasible until we overcome the main challenges regarding data privacy and security, quality assessment, and the reproducibility and uncertainty of AI results. Moreover, there is a lack of both standardized methods to measure the clinical outcomes of mHealth apps and techniques to encourage user engagement and behavior changes in the long term. We expect that in the near future, these obstacles will be overcome and that the ongoing European project, Watching the risk factors (WARIFA), will provide considerable advances in the implementation of AI-based mHealth apps for disease prevention and health promotion.

show abstract

“…However, many real-world applications present high-dimensional and heterogeneous data (mixed-type) with numerical and categorical features. SMOTEN, a variant of SMOTE for categorical data has been used in various applications [10], however, the quality of generated synthetic data is not the best [14][15][16].…”

Section: Of 22mentioning

confidence: 99%

Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-hoc Interpretability of the Risk Factors

García-Vicente¹,

Chushig-Muzo²,

Jiménez³

et al. 2023

Preprint

View full text Add to dashboard Cite

Machine Learning (ML) methods have become important to enhance the performance of decision-support predictive models. However, class imbalance is one of the main challenges for developing ML models, because it limits the generalization of these models, and biases the learning algorithms. In this paper, we consider oversampling methods for generating synthetic categorical clinical data aiming to improve the predictive performance in ML models, and the identification of risk factors for cardiovascular diseases (CVDs). We performed a comparative study of several categorical synthetic data generation methods, including Generative Adversarial Networks (GANs). Then, we assessed the impact of combining oversampling strategies and linear and nonlinear supervised ML methods. Lastly, we conducted a post-hoc model interpretability based on the importance of the risk factors. Experimental results show the potential of GAN-based models for generating high-quality categorical synthetic data, yielding probability mass functions that are highly close to real data, maintaining relevant insights, and contributing to increase the predictive performance. The GAN-based model and a linear classifier outperforms other oversampling techniques, improving 2\% the area under the curve. These results demonstrate the capability of synthetic data to help both in determining risk factors and building models for CVD prediction.

show abstract

Clinical Synthetic Data Generation to Predict and Identify Risk Factors for Cardiovascular Diseases

Cited by 7 publications

References 39 publications

Untitled

Untitled

Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges

Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-hoc Interpretability of the Risk Factors

Contact Info

Product

Resources

About