[Paper Review] Enhancing Cardiovascular Disease Risk Prediction with Machine Learning Models

Farnoush Shishehbori, Zainab Awan|arXiv (Cornell University)|Jan 29, 2024

Artificial Intelligence in Healthcare7 citations

TL;DR

The paper surveys how machine learning and deep learning models can improve cardiovascular disease risk prediction beyond traditional scores, discussing datasets, methods, challenges, and clinical implications.

ABSTRACT

Cardiovascular disease remains a leading global cause of mortality, necessitating accurate risk prediction tools. Traditional methods, such as QRISK and the Framingham heart score, exhibit limitations in their ability to incorporate comprehensive patient data, potentially resulting in incomplete risk factor consideration. To address these shortcomings, this study conducts a meticulous review focusing on the application of machine learning models to enhance predictive accuracy. Machine learning models, such as support vector machines, and Random Forest, as well as deep learning techniques like convolutional neural networks and recurrent neural networks, have emerged as promising alternatives. These models offer superior performance, accommodating a broader spectrum of variables and providing precise subgroup-specific predictions. While machine learning integration holds promise for enhancing risk assessment, it presents challenges such as data requirements and computational constraints. Additionally, large language models have revolutionised healthcare applications, augmenting diagnostic precision and patient care. This study examines the core aspects of cardiovascular disease event risk and presents a thorough review of traditional and machine learning models, alongside deep learning techniques, for improved accuracy. It offers a comprehensive survey of relevant datasets, critically compares ML models with conventional approaches, and synthesizes key findings, highlighting their implications for clinical practice. Furthermore, the potential of machine learning and large language models in cardiovascular medicine is undeniable. However, rigorous validation and optimisation are imperative before widespread application in healthcare. This integration promises more accurate and personalised cardiovascular care.

Motivation & Objective

• Motivate the need for improved CVD risk prediction beyond traditional tools like QRISK and Framingham.
• Evaluate how machine learning models can incorporate a broader set of patient data for better accuracy.
• Survey a range of models (SVM, Random Forest, CNNs, RNNs) and their applicability to CVD risk.
• Highlight challenges in data requirements, computation, and clinical translation, plus the potential role of large language models.

Proposed method

• Review traditional CVD risk models and their limitations in incorporating diverse patient data.
• Summarize and compare machine learning and deep learning approaches used for CVD risk prediction.
• Discuss data sources and datasets relevant to ML-based CVD risk assessment.
• Critically analyze model performance, generalizability, and clinical integration considerations.
• Explore the role of large language models in augmenting cardiovascular medicine.
• Synthesize findings to provide guidance for validation and optimization before clinical deployment.

Experimental results

Research questions

• RQ1 What are the limitations of traditional cardiovascular risk scores in capturing comprehensive patient data?
• RQ2 Which machine learning and deep learning models show promise for improving CVD risk prediction, and under what data conditions?
• RQ3 What are the main challenges in data requirements, computation, and validation for ML-based CVD risk tools?
• RQ4 How can large language models contribute to cardiovascular risk prediction and clinical practice?
• RQ5 What considerations are necessary for rigorous validation and optimization before clinical adoption?

Key findings

• ML and DL models can accommodate a broader range of variables than traditional scores, potentially improving prediction accuracy.
• There are methodological and data-related challenges, including data requirements and computational constraints.
• Large language models have the potential to augment diagnostic precision and patient care in cardiovascular medicine.
• Rigorous validation and optimization are essential before widespread clinical deployment.
• The study provides a comprehensive survey of datasets and compares ML approaches with conventional methods.
• There is potential for more personalized cardiovascular care with ML, contingent on robust validation.