From Data to Diagnosis: How Machine Learning Is Changing Heart Health Monitoring

Staszak, Katarzyna; Tylkowski, Bartosz; Staszak, Maciej

doi:10.3390/ijerph20054605

Cited by 8 publications

(8 citation statements)

References 70 publications

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“…This comparison could include a detailed explanation of parameters and FLOPS used during the training. In the literature, researchers have claimed to achieve up to 95% accuracy on heart disease datasets [12][13][14][15][16][17][18][19][20][21][22]59,60]. However, we did not have a chance to repeat the results of these studies since the code and configurations were not shared.…”

Section: Discussionmentioning

confidence: 99%

“…WEKA, based on Java, was one of the more commonly referenced tools in the papers by Hazra et al, Khan et al,. Singh et al used WEKA for predicting heart disease, with a dataset of 303 records and a multilayer perceptron neural network (MLPNN) with backpropagation [6,[16][17][18][19].…”

Section: Related Workmentioning

confidence: 99%

“…These systems are increasingly being applied in the field of medical diagnosis, including for the detection and analysis of diseases [16,17]. The data from these systems are also being utilized in machine/deep learning models for improved diagnosis [18,19].…”

Section: Related Workmentioning

confidence: 99%

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Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction

Paladino,

Hughes,

Perera

et al. 2023

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Globally, over 17 million people annually die from cardiovascular diseases, with heart disease being the leading cause of mortality in the United States. The ever-increasing volume of data related to heart disease opens up possibilities for employing machine learning (ML) techniques in diagnosing and predicting heart conditions. While applying ML demands a certain level of computer science expertise—often a barrier for healthcare professionals—automated machine learning (AutoML) tools significantly lower this barrier. They enable users to construct the most effective ML models without in-depth technical knowledge. Despite their potential, there has been a lack of research comparing the performance of different AutoML tools on heart disease data. Addressing this gap, our study evaluates three AutoML tools—PyCaret, AutoGluon, and AutoKeras—against three datasets (Cleveland, Hungarian, and a combined dataset). To evaluate the efficacy of AutoML against conventional machine learning methodologies, we crafted ten machine learning models using the standard practices of exploratory data analysis (EDA), data cleansing, feature engineering, and others, utilizing the sklearn library. Our toolkit included an array of models—logistic regression, support vector machines, decision trees, random forest, and various ensemble models. Employing 5-fold cross-validation, these traditionally developed models demonstrated accuracy rates spanning from 55% to 60%. This performance is markedly inferior to that of AutoML tools, indicating the latter’s superior capability in generating predictive models. Among AutoML tools, AutoGluon emerged as the superior tool, consistently achieving accuracy rates between 78% and 86% across the datasets. PyCaret’s performance varied, with accuracy rates from 65% to 83%, indicating a dependency on the nature of the dataset. AutoKeras showed the most fluctuation in performance, with accuracies ranging from 54% to 83%. Our findings suggest that AutoML tools can simplify the generation of robust ML models that potentially surpass those crafted through traditional ML methodologies. However, we must also consider the limitations of AutoML tools and explore strategies to overcome them. The successful deployment of high-performance ML models designed via AutoML could revolutionize the treatment and prevention of heart disease globally, significantly impacting patient care.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction

Paladino,

Hughes,

Perera

et al. 2023

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“…AI calculations have been able to distinguish early indications of cardiovascular events like arrhythmias and ischemia, as shown by a study by Hannun et al in 2019, which displayed earlier detections of atrial fibrillation from the ECG data being provided [27]. AI has already played a part in monitoring patients in all types of settings as various devices have features to monitor vital signs while providing real-time feedback [45]. A study introduced a novel algorithm combining two event-related moving averages (TERMA) and fractional Fourier transform (FrFT) for enhanced analysis of ECG signals, improving the accuracy in locating peak positions and diagnosing heart diseases.…”

Section: Procedures Time and Complication Rate Reductionmentioning

confidence: 99%

The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature

Gala,

Behl,

Shah

et al. 2024

Healthcare

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Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.

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“…AI algorithms provide standardized guidelines and measurements, automating the generation of echocardiography reports [54,55]. Additionally, they identify errors and inconsistencies in interpretations, flagging measurement discrepancies and highlighting abnormalities [56][57][58][59]. This ensures thorough and guidelinealigned interpretations, enhancing research outcomes and evidence-based practice.…”

Section: Benefits and Implicationsmentioning

confidence: 99%

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

Vasile,

Iriart

2023

Diagnostics

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Advancements in artificial intelligence (AI) have rapidly transformed various sectors, and the field of echocardiography is no exception. AI-driven technologies hold immense potential to revolutionize echo labs’ diagnostic capabilities and improve patient care. This paper explores the importance for echo labs to embrace AI and stay ahead of the curve in harnessing its power. Our manuscript provides an overview of the growing impact of AI on medical imaging, specifically echocardiography. It highlights how AI-driven algorithms can enhance image quality, automate measurements, and accurately diagnose cardiovascular diseases. Additionally, we emphasize the importance of training echo lab professionals in AI implementation to optimize its integration into routine clinical practice. By embracing AI, echo labs can overcome challenges such as workload burden and diagnostic accuracy variability, improving efficiency and patient outcomes. This paper highlights the need for collaboration between echocardiography laboratory experts, AI researchers, and industry stakeholders to drive innovation and establish standardized protocols for implementing AI in echocardiography. In conclusion, this article emphasizes the importance of AI adoption in echocardiography labs, urging practitioners to proactively integrate AI technologies into their workflow and take advantage of their present opportunities. Embracing AI is not just a choice but an imperative for echo labs to maintain their leadership and excel in delivering state-of-the-art cardiac care in the era of advanced medical technologies.

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From Data to Diagnosis: How Machine Learning Is Changing Heart Health Monitoring

Cited by 8 publications

References 70 publications

Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction

Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction

The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

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