ArticlePhysiological genomics2020
Machine learning-based classification and diagnosis of clinical cardiomyopathies.
Article in Physiological genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
16 citing papers in PubMed, 29 citations in OpenAlex.
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- Detection of Left Ventricular Outflow Obstruction From Standard B-Mode Echocardiogram Videos Using Deep Learning.JACC. Advances · 2026Article
- Machine learning-based prediction models for atopic dermatitis diagnosis and evaluation.Fundamental research · 2025Article
- Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.Journal of translational medicine · 2025Review
- Diverse Concepts in Definitions of Dilated Cardiomyopathy: Theory and Practice.Cardiology research · 2024Review
- Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.EClinicalMedicine · 2024Article
- UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review.Reviews in cardiovascular medicine · 2024Review
- Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review.Cureus · 2024Review
- Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.Journal of Korean medical science · 2023Review
- Echocardiography-based machine learning algorithm for distinguishing ischemic cardiomyopathy from dilated cardiomyopathy.BMC cardiovascular disorders · 2023Article
- Artificial Intelligence-Based Methods for Precision Cardiovascular Medicine.Journal of personalized medicine · 2023Review
- Artificial Intelligence in Medicine: Microbiome-Based Machine Learning for Phenotypic Classification.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study.JMIR medical education · 2022Article
- Predictive value of electrocardiographic markers in children with dilated cardiomyopathy.Frontiers in pediatrics · 2022Review
- Genomics of Human Fibrotic Diseases: Disordered Wound Healing Response.International journal of molecular sciences · 2020Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common types of cardiomyopathies leading to heart failure. Accurate diagnostic classification of different types of cardiomyopathies is critical for precision medicine in clinical practice. In this study, we hypothesized that machine learning (ML) can be used as a novel diagnostic approach to analyze cardiac transcriptomic data for classifying clinical cardiomyopathies. RNA-Seq data of human left ventricle tissues were collected from 41 DCM patients, 47 ICM patients, and 49 nonfailure controls (NF) and tested using five ML algorithms: support vector machine with radial kernel (svmRadial), neural networks with principal component analysis (pcaNNet), decision tree (DT), elastic net (ENet), and random forest (RF). Initial ML classifications achieved ~93% accuracy (svmRadial) for NF vs. DCM, ~82% accuracy (RF) for NF vs. ICM, and ~80% accuracy (ENet and svmRadial) for DCM vs. ICM. Next, 50 highly contributing genes (HCGs) for classifying NF and DCM, 68 HCGs for classifying NF and ICM, and 59 HCGs for classifying DCM and ICM were selected for retraining ML models. Impressively, the retrained models achieved ~90% accuracy (RF) for NF vs. DCM, ~90% accuracy (pcaNNet) for NF vs. ICM, and ~85% accuracy (pcaNNet and RF) for DCM vs. ICM. Pathway analyses further confirmed the involvement of those selected HCGs in cardiac dysfunctions such as cardiomyopathies, cardiac hypertrophies, and fibrosis. Overall, our study demonstrates the promising potential of using artificial intelligence via ML modeling as a novel approach to achieve a greater level of precision in diagnosing different types of cardiomyopathies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.