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Machine Learning Analytics for Cardiovascular Diseases
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Machine Learning Analytics for Cardiovascular Diseases

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 4231

Special Issue Editor

Dr. Chao-Yu Guo

E-Mail Website
Guest Editor
Institute of Public Health, National Yang Ming Chao Tung University, Taipei 112, Taiwan
Interests: biostatistics; machine learning; artificial neural networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We seek early applications of machine learning and artificial intelligence (AI) related to cardiovascular diseases and aim to provide a review of pioneering applications of AI in cardiology. Particular areas of interest include AI in cardiovascular disorders, with applications such as prediction tools, screening tools, natural language processing in healthcare and medical informatics, imaging processing, and clinical decision making. We look forward to novel models based on machine learning and artificial neural networks for predicting, analyzing, or classifying cardiovascular disease.

Dr. Chao-Yu Guo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • artificial neural networks
  • AI
  • cardiovascular disorder

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Published Papers (2 papers)

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Research

15 pages, 1984 KiB  
Article
Impact of Integrating Machine Learning in Comparative Effectiveness Research of Oral Anticoagulants in Patients with Atrial Fibrillation
by Sola Han and Hae Sun Suh
Int. J. Environ. Res. Public Health 2022, 19(19), 12916; https://doi.org/10.3390/ijerph191912916 - 9 Oct 2022
Cited by 2 | Viewed by 1659
Abstract
We aimed to compare the ability to balance baseline covariates and explore the impact of residual confounding between conventional and machine learning approaches to derive propensity scores (PS). The Health Insurance Review and Assessment Service database (January 2012–September 2019) was used. Patients with [...] Read more.
We aimed to compare the ability to balance baseline covariates and explore the impact of residual confounding between conventional and machine learning approaches to derive propensity scores (PS). The Health Insurance Review and Assessment Service database (January 2012–September 2019) was used. Patients with atrial fibrillation (AF) who initiated oral anticoagulants during July 2015–September 2018 were included. The outcome of interest was stroke/systemic embolism. To estimate PS, we used a logistic regression model (i.e., a conventional approach) and a generalized boosted model (GBM) which is a machine learning approach. Both PS matching and inverse probability of treatment weighting were performed. To evaluate balance achievement, standardized differences, p-values, and boxplots were used. To explore residual confounding, E-values and negative control outcomes were used. In total, 129,434 patients were identified. Although all baseline covariates were well balanced, the distribution of continuous variables seemed more similar when GBM was applied. E-values ranged between 1.75 and 2.70 and were generally higher in GBM. In the negative control outcome analysis, slightly more nonsignificant hazard ratios were observed in GBM. We showed GBM provided a better ability to balance covariates and had a lower impact of residual confounding, compared with the conventional approach in the empirical example of comparative effectiveness analysis. Full article
(This article belongs to the Special Issue Machine Learning Analytics for Cardiovascular Diseases)
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9 pages, 1814 KiB  
Article
A Novel Algorithm to Estimate the Significance Level of a Feature Interaction Using the Extreme Gradient Boosting Machine
by Chao-Yu Guo and Ke-Hao Chang
Int. J. Environ. Res. Public Health 2022, 19(4), 2338; https://doi.org/10.3390/ijerph19042338 - 18 Feb 2022
Cited by 10 | Viewed by 1929
Abstract
Recent studies have revealed the importance of the interaction effect in cardiac research. An analysis would lead to an erroneous conclusion when the approach failed to tackle a significant interaction. Regression models deal with interaction by adding the product of the two interactive [...] Read more.
Recent studies have revealed the importance of the interaction effect in cardiac research. An analysis would lead to an erroneous conclusion when the approach failed to tackle a significant interaction. Regression models deal with interaction by adding the product of the two interactive variables. Thus, statistical methods could evaluate the significance and contribution of the interaction term. However, machine learning strategies could not provide the p-value of specific feature interaction. Therefore, we propose a novel machine learning algorithm to assess the p-value of a feature interaction, named the extreme gradient boosting machine for feature interaction (XGB-FI). The first step incorporates the concept of statistical methodology by stratifying the original data into four subgroups according to the two interactive features. The second step builds four XGB machines with cross-validation techniques to avoid overfitting. The third step calculates a newly defined feature interaction ratio (FIR) for all possible combinations of predictors. Finally, we calculate the empirical p-value according to the FIR distribution. Computer simulation studies compared the XGB-FI with the multiple regression model with an interaction term. The results showed that the type I error of XGB-FI is valid under the nominal level of 0.05 when there is no interaction effect. The power of XGB-FI is consistently higher than the multiple regression model in all scenarios we examined. In conclusion, the new machine learning algorithm outperforms the conventional statistical model when searching for an interaction. Full article
(This article belongs to the Special Issue Machine Learning Analytics for Cardiovascular Diseases)
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