Empirical Analysis of Machine Learning Algorithms on Imbalance Electrocardiogram Based Arrhythmia Dataset for Heart Disease Detection

Ketu, Shwet; Mishra, Pramod Kumar

doi:10.1007/s13369-021-05972-2

Cited by 56 publications

(22 citation statements)

References 53 publications

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“…First, some manipulations can be applied on initial data, such as random scaling, flipping, shifting, and noising ECG, to achieve accurate detection of multiple arrhythmias ( Vicar et al, 2020 ; Nonaka and Seita, 2021 ; Do et al, 2022 ). The same application can profit from using the synthetic samples generated from the training ones using intuitive adaptive synthetic data sampling (ADASYN, Virgeniya and Ramaraj, 2021 ) or synthetic minority oversampling technique (SMOTE, Ketu and Mishra, 2021 ). Data samples can be generated artificially by specially trained ML or DL models (such as Gaussian mixture model (GMM), generative adversarial network (GAN), LSTM/biLSTM, CNN), as has been shown for time-series ECG (including dependent multichannel signals) and 2D spectrogram applications (e.g., Lima et al, 2019 ; Brophy, 2020 ; Hatamian et al, 2020 ; Hazra and Byun, 2020 ).…”

Section: Ecg Analysismentioning

confidence: 99%

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

et al. 2022

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Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

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Section: Ecg Analysismentioning

confidence: 99%

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

et al. 2022

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“…Automatic diagnosis systems and various recognition systems are based on CNN architectures. Similarly, the medical field is also acquiring major contributions, including the detection of diseases such as malaria detection [49], heart disease detection [50], and arrhythmia detection [51]. CNN architectures are also used in microbial detections where nuclei segmentation is a major example in this field [52,53], and these neural networks can also be widely used across the field of chronic diseases and the health care sector [54,55].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Diagnosis of Intracranial Tumors via the Selective CNN Data Modeling Technique

et al. 2022

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A brain tumor occurs in humans when a normal cell turns into an aberrant cell inside the brain. Primarily, there are two types of brain tumors in Homo sapiens: benign tumors and malignant tumors. In brain tumor diagnosis, magnetic resonance imaging (MRI) plays a vital role that requires high precision and accuracy for diagnosis, otherwise, a minor error can result in severe consequences. In this study, we implemented various configured convolutional neural network (CNN) paradigms on brain tumor MRI scans that depict whether a person is a brain tumor patient or not. This paper emphasizes objective function values (OFV) achieved by various CNN paradigms with the least validation cross-entropy loss (LVCEL), maximum validation accuracy (MVA), and training time (TT) in seconds, which can be used as a feasible tool for clinicians and the medical community to recognize tumor patients precisely. Experimentation and evaluation were based on a total of 2189 brain MRI scans, and the best architecture shows the highest accuracy of 0.8275, maximum objective function value of 1.84, and an area under the ROC (AUC-ROC) curve of 0.737 to accurately recognize and classify whether or not a person has a brain tumor.

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“…Over time experts and practitioners have shown keen interest in diagnosing heart disease by employing classical machine learning techniques. Experts usually utilize a classification approach to create a heart disease diagnosis model in their research study [5,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38].…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Machine Learning Model Based on Improved Features Selections for Early and Accurate Heart Disease Predication

Ullah

Chen

Rajab

et al. 2022

Computational Intelligence and Neuroscience

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Coronary heart disease has an intense impact on human life. Medical history-based diagnosis of heart disease has been practiced but deemed unreliable. Machine learning algorithms are more reliable and efficient in classifying, e.g., with or without cardiac disease. Heart disease detection must be precise and accurate to prevent human loss. However, previous research studies have several shortcomings, for example,take enough time to compute while other techniques are quick but not accurate. This research study is conducted to address the existing problem and to construct an accurate machine learning model for predicting heart disease. Our model is evaluated based on five feature selection algorithms and performance assessment matrix such as accuracy, precision, recall, F1-score, MCC, and time complexity parameters. The proposed work has been tested on all of the dataset'sfeatures as well as a subset of them. The reduction of features has an impact on theperformance of classifiers in terms of the evaluation matrix and execution time. Experimental results of the support vector machine, K-nearest neighbor, and logistic regression are 97.5%,95 %, and 93% (accuracy) with reduced computation timesof 4.4, 7.3, and 8seconds respectively.

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Empirical Analysis of Machine Learning Algorithms on Imbalance Electrocardiogram Based Arrhythmia Dataset for Heart Disease Detection

Cited by 56 publications

References 53 publications

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

Diagnosis of Intracranial Tumors via the Selective CNN Data Modeling Technique

An Efficient Machine Learning Model Based on Improved Features Selections for Early and Accurate Heart Disease Predication

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