Machine learning for cardiology

Abstract

This paper reviews recent cardiology literature and reports how Artificial Intelligence Tools (specifically, Machine Learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine Learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in Machine Learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying Machine Learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while Neural Networks are slowly being incorporated in cardiovascular research, other important techniques such as Semi-Supervised Learning and Federated Learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.

Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches

“…Complexity and privacy concerns are the main barriers to access medical data and prevent training very complex models, such as DNNs. 33 Although DNN was used only in five studies, it performed exceptionally well and achieved a high AUC (90%) in three studies. ►Table 3 summarizes the best DNNs performance for predicting MACE, in-hospital mortality as well as 1-year and 30-day mortality.…”

“…The Boosting technique usually provides very accurate models. 33 In fact, Boosting Ensemble technique sequentially combines multiple ML models with high bias models to correct the predictions of models, obtain better predictions, 34 and counterbalance overfitting. 55 XGBoost was the most frequently used technique and obtained the best performance among Boosting Ensemble techniques.…”

“…32 Semi-supervised learning attempts to develop an algorithm when a few samples are labeled. 33 Supervised learning algorithms include LR, K-nearest neighbors (KNN), support vector machine (SVM), Naive Bayes (NB), Decision Trees (DTs), RF, Boosting Ensemble method, and artificial neural networks (ANNs). 31 Deep neural network (DNN) can categorize both supervised and unsupervised ML.…”

Predicting Coronary Heart Disease Using an Improved LightGBM Model: Performance Analysis and Comparison

“…Early detection of CHD can improve the cure probability and can decrease the cost of treatment. Numerous machine learning algorithms and data mining technologies have been widely used in the medical field [2]- [6] in recent years, owing to advancements in machine learning algorithms and a significant reduction in the cost of data storage. Data mining technology has become essential for healthcare data mining, such as disease diagnosis, auxiliary diagnosis, drug mining, and biomedicine.…”

The Value of the Gensini Score For Prognostic Assessment in Patients with Acute Coronary Syndrome--A Retrospective Cohort Study Based on Machine Learning Methods

“…However, they have not yet developed a valid GS-based predictive model for patients with CAD. Nowadays, Machine Learning (ML) models based on Artificial Intelligence (AI) theory are relatively mature in medicine [15,16]. ML has the advantage of being good at extracting non-linear relationships between different variables and showing researchers the weighting relationships by feature maps.…”