Analysis of ECG Arrhythmia for Heart Disease Detection  using SVM and Cuckoo Search Optimized Neural Network

Patil, Dinesh; Singh, Rhishi Pratap; Thakare, V. M.; Gulve, Avinash K.

doi:10.14419/ijet.v7i2.17.11553

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…As previously indicated, there were several inconsistencies in terms of assessment measures published by the literature. For example, some research reported their results with accuracy [ 45 ]; others provided with accuracy, precision, recall, and F1-score [ 42 ]; while a few studies emphasized sensitivity, specificity, and true positive [ 67 ]. As a result, there were no criteria for the authors to follow in order to report their findings correctly and genuinely.…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

2022

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Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.

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Section: Discussionmentioning

confidence: 99%

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

2022

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“…Within this endeavour, the researchers harnessed the potential of machine learning approaches, including Support Vector Machine (SVM) and the ingenious Cuckoo Search-Optimized Neural Network. The results were impressive, with the support vector machine yielding an enhanced accuracy rate of 94.44% [18].…”

Section: Related Studiesmentioning

confidence: 99%

Prognostic Modeling of Chronic Kidney Disease Progression: Bridging Mild and Severe Stages through a Machine Learning Approach

Bah,

Jallow,

Bah

2023

austinjclincaserep

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Background and Aim: Chronic Kidney Disease (CKD) is a condition where the kidneys gradually lose their ability to function properly over time. It is into stages based on the severity of kidney damage and the level of kidney function. The objective of our study is to employ machine learning models for the prediction of Chronic Kidney Disease (CKD) progression. Methods: Our study is centered on the prediction of CKD progression from mild (I, II, III) to advanced stages (IV, V, VI). We utilized logistic regression with a lasso-penalized approach and random forest model for our predictive analysis. We assessed the significance of features using the Gini index derived from the random forest model. The performance of our models was evaluated based on the Area Under Receiver Operating Characteristic (AU-ROC), AU-Precision-Recall (PR) curves, recall, precision and accuracy. Results: Our study showcases remarkable predictive performance of CKD progression from milder (I, II, III) to severe stages (IV, V, VI). Random forest model achieved an accuracy of 85%, a recall rate of 86%, a precision rate of 83%, an AU-ROC score of 92%, and an AU-PR score of 83%. The logistic regression model exhibited an accuracy of 84%, a recall rate of 84%, a precision rate of 85%, an AU-ROC score of 92%, and an AU-PR score of 81%. Regarding variable importance, our model identifies creatinine as the most critical feature, followed by eGFR. Conclusion: Our findings indicate that machine learning models hold promise in predicting CKD progression with substantial discriminative capabilities, as evidenced by high AUROC curves. This suggests their potential utility in real-world clinical settings for identifying patients at risk of transitioning from mild to severe stages of CKD.

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“…The results showed that the backpropagation neural network and Logistic Regression algorithms achieved an accuracy of 85.074% and 92.58%, respectively. The work in [129] encompasses two domains: signal processing and statistical learning. Leveraging signal processing techniques, authors have successfully segmented and represented each heartbeat through a vector of distinctive characteristics.…”

Section: Machine Learning Models For Cardiovascular Disease Predictio...mentioning

confidence: 99%

A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

Naser,

Majeed,

Alsabah

et al. 2024

Algorithms

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Cardiovascular disease is the leading cause of global mortality and responsible for millions of deaths annually. The mortality rate and overall consequences of cardiac disease can be reduced with early disease detection. However, conventional diagnostic methods encounter various challenges, including delayed treatment and misdiagnoses, which can impede the course of treatment and raise healthcare costs. The application of artificial intelligence (AI) techniques, especially machine learning (ML) algorithms, offers a promising pathway to address these challenges. This paper emphasizes the central role of machine learning in cardiac health and focuses on precise cardiovascular disease prediction. In particular, this paper is driven by the urgent need to fully utilize the potential of machine learning to enhance cardiovascular disease prediction. In light of the continued progress in machine learning and the growing public health implications of cardiovascular disease, this paper aims to offer a comprehensive analysis of the topic. This review paper encompasses a wide range of topics, including the types of cardiovascular disease, the significance of machine learning, feature selection, the evaluation of machine learning models, data collection & preprocessing, evaluation metrics for cardiovascular disease prediction, and the recent trends & suggestion for future works. In addition, this paper offers a holistic view of machine learning’s role in cardiovascular disease prediction and public health. We believe that our comprehensive review will contribute significantly to the existing body of knowledge in this essential area.

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Analysis of ECG Arrhythmia for Heart Disease Detection using SVM and Cuckoo Search Optimized Neural Network

Cited by 9 publications

References 12 publications

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

Prognostic Modeling of Chronic Kidney Disease Progression: Bridging Mild and Severe Stages through a Machine Learning Approach

A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

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