Supervised Machine Learning Algorithm for Detection of Cardiac Disorders

Borkar, Sneha; Annadate, Mrunal Ninad

doi:10.1109/iccubea.2018.8697795

Cited by 18 publications

(3 citation statements)

References 4 publications

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“…Support vector machine learning models significantly outperform all other classifiers in the study of cardiovascular breakdown recognition in [6], which investigated the cardiovascular meltdown rate with the assistance of a distance distribution matrix, computational complexity neural net model, and faint heartbeat measurement differential evaluation. Automatic dilated cardiomyopathy AND atrial septal defect illness identification have been offered as a method for dealing with the distinction between cardiovascular diseases using directed machine learning classifiers [7]. Using the controlled help vector regression ( SVR algorithm, the separated highlighting is sorted.…”

Section: IImentioning

confidence: 99%

Using Machine Learning Classifiers, Analyze and Predict Cardiovascular Disease

Vardhan¹,

Kumari²

2022

IJRASET

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A myocardial infarction, indigestion, or even death can take place as a result of several illnesses known as heart disease, including restricted or blocked veins. Depending on the extent of the patient's side effects, the condition is anticipated by the supervised classification classifier. This research intends to investigate how Machine Learning Tree Classifiers depict Heart Disease Prediction. Pattern recognition tree classifiers are analyzed using Random Forest, Decision Tree, Logistic Regression, Support Vector Machine (SVM), and K-nearest Neighbors (KNN) based on their correctness and AUC Gryphon scores. With an execution time of 1.32 seconds, better precision of 85%, and a Coefficient Of determination (r score of 0.8739, the Random Forest machine learning classification surpassed its effectiveness in this investigation of coronary heart disease detection.

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Section: IImentioning

confidence: 99%

Using Machine Learning Classifiers, Analyze and Predict Cardiovascular Disease

Vardhan¹,

Kumari²

2022

IJRASET

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“…More recently Alis et al [15] exploited a machine learning approach for texture feature analysis of cardiac magnetic resonance imaging (MRI) for examining the incidence of ventricular tachyarrhythmia (VT) in hypertrophic cardiomyopathy patients. Similarly, Borkar et al [16] proposed a machine learning approach for the automatic detection of Atrial Septal Defect (ASD) and dilated cardiomyopathy (DCM) diseases. Their dataset comprised of the ultrasound videos of DCM, ASD, and normal cases.…”

Section: Literature Reviewmentioning

confidence: 99%

CNN-KCL: Automatic Myocarditis Diagnosis using Convolutional Neural Network Combined with K-means Clustering

Sharifrazi¹,

Alizadehsani²,

Joloudari³

et al. 2020

Preprint

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Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained as one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered as a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR is heavily dependent on the clinical presentation and non-specific features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose the Myocarditis. The hybrid CNN-KCL method performs the early and accurate diagnosis of Myocarditis. To the best-of-our-knowledge, a Convolutional neural network has never been used before for the diagnosis of Myocarditis. In this study, we used 47 subjects to diagnose myocarditis patients from Tehran's Omid Hospital. The total number of data examined is 10425. Our results demonstrate that CNN-KCL achieves 92.3% in terms of diagnosis myocarditis prediction accuracy which is significantly better than those reported in previous studies.

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“…Machine learning techniques have been used to predict cardiac-related prognosis in different types of patients ( Guisen et al, 2021 ), for the prediction of complications after cardiovascular surgery ( Jiang1 et al, 2021 ), and for the prediction of the progression of heart failure in hypertrophic cardiomyopathy ( Fahmy et al, 2021 ). Support vector machine-based algorithms have been explored for the detection of cardiac disorders ( Wang and Zheng, 2009 ; Borkar and Annadate, 2018 ). Differences in the cardiovascular autonomic regulation of ischemic and dilated cardiomyopathy patients have been hypothesized ( Freeman et al, 2006 ).…”

Section: Introductionmentioning

confidence: 99%

Baroreflex activity through the analysis of the cardio-respiratory variability influence over blood pressure in cardiomyopathy patients

Rodriguez,

Schulz,

Voss

et al. 2023

Front. Physiol.

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A large portion of the elderly population are affected by cardiovascular diseases. Early prognosis of cardiomyopathies remains a challenge. The aim of this study was to classify cardiomyopathy patients by their etiology based on significant indexes extracted from the characterization of the baroreflex mechanism in function of the influence of the cardio-respiratory activity over the blood pressure. Forty-one cardiomyopathy patients (CMP) classified as ischemic (ICM—24 patients) and dilated (DCM—17 patients) were considered. In addition, thirty-nine control (CON) subjects were used as reference. The beat-to-beat (BBI) time series, from the electrocardiographic (ECG) signal, the systolic (SBP), and diastolic (DBP) time series, from the blood pressure signal (BP), and the respiratory time (TT), from the respiratory flow (RF) signal, were extracted. The three-dimensional representation of the cardiorespiratory and vascular activities was characterized geometrically, by fitting a polygon that contains 95% of data, and by statistical descriptive indices. DCM patients presented specific patterns in the respiratory response to decreasing blood pressure activity. ICM patients presented more stable cardiorespiratory activity in comparison with DCM patients. In general, CMP shown limited ability to regulate changes in blood pressure. In addition, patients also shown a limited ability of their cardiac and respiratory systems response to regulate incremental changes of the vascular variability and a lower heart rate variability. The best classifiers were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 92.7% accuracy, 94.1% sensitivity, and 91.7% specificity. When comparing CMP patients and CON subjects, the best model achieved 86.2% accuracy, 82.9% sensitivity, and 89.7% specificity. When comparing ICM patients and CON subjects, the best model achieved 88.9% accuracy, 87.5% sensitivity, and 89.7% specificity. When comparing DCM patients and CON subjects, the best model achieved 87.5% accuracy, 76.5% sensitivity, and 92.3% specificity. In conclusion, this study introduced a new method for the classification of patients by their etiology based on new indices from the analysis of the baroreflex mechanism.

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Supervised Machine Learning Algorithm for Detection of Cardiac Disorders

Cited by 18 publications

References 4 publications

Using Machine Learning Classifiers, Analyze and Predict Cardiovascular Disease

Using Machine Learning Classifiers, Analyze and Predict Cardiovascular Disease

CNN-KCL: Automatic Myocarditis Diagnosis using Convolutional Neural Network Combined with K-means Clustering

Baroreflex activity through the analysis of the cardio-respiratory variability influence over blood pressure in cardiomyopathy patients

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