Machine Learning Approach on High Risk Treadmill Exercise Test to Predict Obstructive Coronary Artery Disease by using P, QRS, and T waves’ Features

Yılmaz, Abdurrahim; Hayıroğlu, Mert İlker; Pay, Levent; Demirçalı, Ali Anıl; Coşkun, Cahit; Varol, Rahmetullah; Tezen, Ozan; Eren, Semih; Çetin, Tuğba; Tekkeşin, Ahmet İlker; Üvet, Hüseyin

doi:10.1016/j.cpcardiol.2022.101482

Cited by 13 publications

(9 citation statements)

References 15 publications

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“…In addition, the algorithms of machine learning have been respected by many researchers due to their accurate determination of feature selection, model construction, and other applications in cardiovascular and renal diseases. 31 , 32 Therefore, this paper identifies differentially expressed tandem genes and explores the biological functions of tandem genes based on HF and CKD microarray datasets. Common diagnostic biomarkers for both diseases were screened using machine learning algorithms and their association with cardiac and renal function was analyzed.…”

Section: Discussionmentioning

confidence: 99%

Identification of Shared Signature Genes and Immune Microenvironment Subtypes for Heart Failure and Chronic Kidney Disease Based on Machine Learning

Wang,

Rao,

Chen

et al. 2024

JIR

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Background A complex interrelationship exists between Heart Failure (HF) and chronic kidney disease (CKD). This study aims to clarify the molecular mechanisms of the organ-to-organ interplay between heart failure and CKD, as well as to identify extremely sensitive and specific biomarkers. Methods Differentially expressed tandem genes were identified from HF and CKD microarray datasets and enrichment analyses of tandem perturbation genes were performed to determine their biological functions. Machine learning algorithms are utilized to identify diagnostic biomarkers and evaluate the model by ROC curves. RT-PCR was employed to validate the accuracy of diagnostic biomarkers. Molecular subtypes were identified based on tandem gene expression profiling, and immune cell infiltration of different subtypes was examined. Finally, the ssGSEA score was used to build the ImmuneScore model and to assess the differentiation between subtypes using ROC curves. Results Thirty-three crosstalk genes were associated with inflammatory, immune and metabolism-related signaling pathways. The machine-learning algorithm identified 5 hub genes (PHLDA1, ATP1A1, IFIT2, HLTF, and MPP3) as the optimal shared diagnostic biomarkers. The expression levels of tandem genes were negatively correlated with left ventricular ejection fraction and glomerular filtration rate. The CIBERSORT results indicated the presence of severe immune dysregulation in patients with HF and CKD, which was further validated at the single-cell level. Consensus clustering classified HF and CKD patients into immune and metabolic subtypes. Twelve immune genes associated with immune subtypes were screened based on WGCNA analysis, and an ImmuneScore model was constructed for high and low risk. The model accurately predicted different molecular subtypes of HF or CKD. Conclusion Five crosstalk genes may serve as potential biomarkers for diagnosing HF and CKD and are involved in disease progression. Metabolite disorders causing activation of a large number of immune cells explain the common pathogenesis of HF and CKD.

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Section: Discussionmentioning

confidence: 99%

Identification of Shared Signature Genes and Immune Microenvironment Subtypes for Heart Failure and Chronic Kidney Disease Based on Machine Learning

Wang,

Rao,

Chen

et al. 2024

JIR

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“…4,17,26,40,57,109 Among some of the challenges to be faced, the incorporation of new technologies (such as those encompassing artificial intelligence) could further change the landscape by optimizing its assessment and scope. 23,110,111 Indeed, as digital tools become increasingly intertwined with several diagnostic modalities, so too may ExECG be affected by these applications. 11,[110][111][112] When considering ExECG in contemporary settings, an integrated assessment should be undertaken focusing not only on the ST segment but also on all the different and interlinked components that take part in the cardiovascular response to exercise and can be dynamically influenced by pathologic processes.…”

Section: Current Challenges and Future Perspectivesmentioning

confidence: 99%

“…23,110,111 Indeed, as digital tools become increasingly intertwined with several diagnostic modalities, so too may ExECG be affected by these applications. 11,[110][111][112] When considering ExECG in contemporary settings, an integrated assessment should be undertaken focusing not only on the ST segment but also on all the different and interlinked components that take part in the cardiovascular response to exercise and can be dynamically influenced by pathologic processes. 17,30,31,52 In this way, data derived from this complex examination can be streamlined as to improve its applicability and relevance while harnessing its full potential.…”

Section: Current Challenges and Future Perspectivesmentioning

confidence: 99%

Section: Current Challenges and Future Perspectivesmentioning

confidence: 99%

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Sixty years of the Bruce protocol: reappraising the contemporary role of exercise stress testing with electrocardiographic monitoring

Vilela,

Oliveira,

Oliveira

et al. 2023

Porto Biomedical Journal

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The cardiovascular response to exercise has long been a focus of interest. Over a century ago, the first descriptions of electrocardiographic changes occurring during exercise highlighted the possible relevance of this dynamic assessment. In this background, the inception of the Bruce protocol circa 60 years ago allowed for a major leap in this field by providing a standardized framework with which to address this issue, by means of an integrated and structured methodology. Since then, exercise stress testing with electrocardiographic monitoring (ExECG) has become one of the most widely appraised tests in cardiovascular medicine. Notably, past few decades have been profoundly marked by substantial advances in the approach to cardiovascular disease, challenging prior notions concerning both its physiopathology and overall management. Among these, the ever-evolving presentations of cardiovascular disease coupled with the development and implementation of several novel diagnostic modalities (both invasive and noninvasive) has led to a shifting paradigm in the application of ExECG. This technique, however, has continuously shown to be of added value across various momentums of the cardiovascular continuum, as depicted in several contemporary guidelines. This review provides a pragmatical reflexion on the development of ExECG, presenting a comprehensive overview concerning the current role of this modality, its challenges, and its future perspectives.

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“…When false-positive rates of TET are high, ML models also have the ability to optimize performance based on ECG wave characteristics and signals. 23 But for all potential applications, high-quality data and model validation on invisible datasets are the keys to success. It has been shown that patients who are likely to have severe CAD on angiography can be distinguished from those without severe angiographic coronary disease and/or miscarriages.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases

Bozyel

2024

Anatol J Cardiol

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Despite all the advancements in science, medical knowledge, healthcare, and the healthcare industry, cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. The main reasons are the inadequacy of preventive health services and delays in diagnosis due to the increasing population, the failure of physicians to apply guide-based treatments, the lack of continuous patient follow-up, and the low compliance of patients with doctors’ recommendations. Artificial intelligence (AI)-based clinical decision support systems (CDSSs) are systems that support complex decision-making processes by using AI techniques such as data analysis, foresight, and optimization. Artificial intelligence-based CDSSs play an important role in patient care by providing more accurate and personalized information to healthcare professionals in risk assessment, diagnosis, treatment optimization, and monitoring and early warning of CVD. These are just some examples, and the use of AI for CVD decision support systems is rapidly evolving. However, for these systems to be fully reliable and effective, they need to be trained with accurate data and carefully evaluated by medical professionals.

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Machine Learning Approach on High Risk Treadmill Exercise Test to Predict Obstructive Coronary Artery Disease by using P, QRS, and T waves’ Features

Cited by 13 publications

References 15 publications

Identification of Shared Signature Genes and Immune Microenvironment Subtypes for Heart Failure and Chronic Kidney Disease Based on Machine Learning

Identification of Shared Signature Genes and Immune Microenvironment Subtypes for Heart Failure and Chronic Kidney Disease Based on Machine Learning

Sixty years of the Bruce protocol: reappraising the contemporary role of exercise stress testing with electrocardiographic monitoring

Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases

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