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Diagnostics
Special Issues
Early Diagnosis of Cardiovascular Diseases: Latest Research
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Early Diagnosis of Cardiovascular Diseases: Latest Research

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: closed (18 November 2022) | Viewed by 2634

Special Issue Editors

Dr. Constantinos Bakogiannis

E-Mail Website
Guest Editor
3rd Cardiology Department, Cardiovascular Prevention and Digital Cardiology Lab Aristotle University of Thessaloniki, Hippokration General Hospital, Thessaloniki, Greece
Interests: cardiovascular prevention; heart failure; sports cardiology; digital cardiology; mHealth; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Panagiotis C. Stafylas

E-Mail Website
Guest Editor
HealThink PC- Medical Research and Innovation, Thessaloniki, Greece
Interests: health technology assessment; health economics; cost-effectiveness; cost-benefit; digital health; remote monitoring; integrated care; hypertension; biomarkers; cardiovascular prevention

Special Issue Information

Dear Colleagues,

Cardiovascular diseases (CVDs), i.e., atherosclerosis, coronary artery disease, heart failure, cardiomyopathies, arrhythmic disorders, valve diseases etc., are leading causes of morbidity and mortality worldwide. Unhealthy diet, physical inactivity, smoking and alcohol abuse are among the most debilitating behavioral risk factors. Genetic factors, population aging and environmental changes due to globalization and urbanization further contribute to the increased burden of CVDs. 

Despite the remarkable progress in pharmacotherapy, CVDs incidence and prevalence seem to be further increasing. How can this be explained? CVDs, especially those related to atherothrombosis, are slowly increasing in incidence and may initiate even at a very young age. Thus, early diagnosis and subsequently earlier intervention when needed are considered key factors in reducing CVD mortality and morbidity.

This Special Issue entitled “Early Diagnosis of Cardiovascular Diseases: Latest Research” focuses on the most recent and most pioneering diagnostic advances in diagnosing cardiovascular diseases, including but not limited to: novel imaging techniques, novel biomarkers for early diagnosis, genomic data, as well as advances in the field of digital cardiology, that is, artificial intelligence/machine learning techniques used for diagnostic purposes.

Dr. Constantinos Bakogiannis
Dr. Panagiotis C. Stafylas
Guest Editors

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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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

  • biomarkers
  • genomics
  • proteomics
  • radiomics
  • cardiovascular imaging
  • artificial intelligence
  • digital cardiology
  • risk classification
  • cardiovascular prevention
  • atherothrombosis

Published Papers (1 paper)

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Research

13 pages, 1216 KiB  
Article
Comparison between Machine Learning and Multiple Linear Regression to Identify Abnormal Thallium Myocardial Perfusion Scan in Chinese Type 2 Diabetes
by Jiunn-Diann Lin, Dee Pei, Fang-Yu Chen, Chung-Ze Wu, Chieh-Hua Lu, Li-Ying Huang, Chun-Heng Kuo, Shi-Wen Kuo and Yen-Lin Chen
Diagnostics 2022, 12(7), 1619; https://doi.org/10.3390/diagnostics12071619 - 3 Jul 2022
Cited by 1 | Viewed by 1748
Abstract
Type 2 diabetes mellitus (T2DM) patients have a high risk of coronary artery disease (CAD). Thallium-201 myocardial perfusion scan (Th-201 scan) is a non-invasive and extensively used tool in recognizing CAD in clinical settings. In this study, we attempted to compare the predictive [...] Read more.
Type 2 diabetes mellitus (T2DM) patients have a high risk of coronary artery disease (CAD). Thallium-201 myocardial perfusion scan (Th-201 scan) is a non-invasive and extensively used tool in recognizing CAD in clinical settings. In this study, we attempted to compare the predictive accuracy of evaluating abnormal Th-201 scans using traditional multiple linear regression (MLR) with four machine learning (ML) methods. From the study, we can determine whether ML surpasses traditional MLR and rank the clinical variables and compare them with previous reports.In total, 796 T2DM, including 368 men and 528 women, were enrolled. In addition to traditional MLR, classification and regression tree (CART), random forest (RF), stochastic gradient boosting (SGB) and eXtreme gradient boosting (XGBoost) were also used to analyze abnormal Th-201 scans. Stress sum score was used as the endpoint (dependent variable). Our findings show that all four root mean square errors of ML are smaller than with MLR, which implies that ML is more precise than MLR in determining abnormal Th-201 scans by using clinical parameters. The first seven factors, from the most important to the least are:body mass index, hemoglobin, age, glycated hemoglobin, Creatinine, systolic and diastolic blood pressure. In conclusion, ML is not inferior to traditional MLR in predicting abnormal Th-201 scans, and the most important factors are body mass index, hemoglobin, age, glycated hemoglobin, creatinine, systolic and diastolic blood pressure. ML methods are superior in these kinds of studies. Full article
(This article belongs to the Special Issue Early Diagnosis of Cardiovascular Diseases: Latest Research)
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