An Analytical Review of Heart Failure Detection based on IoT and Machine Learning

Gahane, Aroh; Chinnaiah, Kotadi

doi:10.1109/icais53314.2022.9742913

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) that is used to analyze time-series and sequential data [9]. LSTMs, unlike standard RNNs, have memory cells and numerous gates, such as input, output, and forget gates, which allow them to learn and store knowledge over extended data sequences [10]. The capacity of LSTMs to grasp long-term relationships and temporal patterns in sequential data is its primary benefit.…”

Section: Introductionmentioning

confidence: 99%

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A Framework of Digital Twins for Improving Respiratory Health and Healthcare Measures

Nancy,

Venkatesan,

Sundar

et al. 2024

SCPE

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The investigation describes an inventive use of digital twin technology and LSTM-based machine learning models for real-time patient lung disease monitoring and nutrition planning. The suggested application uses various patient healthcare data, treatment processes, dietary habits, and real-time sensor information to construct digital twins, which are virtual reproductions of specific patients. The LSTM model is trained on this large dataset to predict patient health improvements and dietary needs. For each patient’s digital twin, the program provides personalized treatment plans and nutritional advice, enabling proactive interventions and optimizing patient care. Using performance measures, the trained LSTM model achieves high scores for accuracy (92%), precision (89%), recall (93%), and F1 score (91%), proving its usefulness in generating credible health predictions. Patient feedback on the program shows that patients (98.8%) agree on the accuracy and importance of health feedback, as well as the convenience of access to health information (95.4%). The application’s response rate study reveals an average response rate of 85.87%, assuring prompt feedback. To secure patient information, the study emphasizes data privacy and security, adopting multilayered authentication and data encryption. The outcomes of this study demonstrate the application’s potential to revolutionize patient-centered healthcare by providing data-driven, personalized solutions to patients and healthcare professionals.

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Section: Introductionmentioning

confidence: 99%

“…Robust feature design was a major component of traditional machine learning models. Choosing, modifying, and combining input variables were all part of the feature engineering, which improved model performance [10]. To increase the accuracy of the model, engineers had to invest a lot of effort in the fine-tuning of characteristics.…”

Section: Introductionmentioning

confidence: 99%

A Framework of Digital Twins for Improving Respiratory Health and Healthcare Measures

Nancy,

Venkatesan,

Sundar

et al. 2024

SCPE

View full text Add to dashboard Cite

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“…By using CNNs, different VGG models, and RNNs, it is possible to improve the capabilities of both the local radiologist and the outsourced team regarding the precise diagnosis of complex cases. It may further ease caseloads for radiologists, enhance efficiency, and ensure the required accuracy of the diagnosis, in turn improving patient outcomes [16], [17].…”

Section: Introductionmentioning

confidence: 99%

A Comparative analysis of Advanced Machine Learning Techniques for Enhancing Brain Tumor Detection

G. Nagaraju

2024

jes

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Detection and classification of brain tumors are prominent tasks in neuroimaging, as it ensures accurate and timely diagnosis for effective treatment planning. This study explores the utility of machine learning models, such as convolutional neural networks, VGG 16 and 19 architectures, and recurrent neural networks, in improving the detection of brain tumor anomaly in both CT and MRI. A total of 3400 images were integrated and collected from multiple sources and prepared for usage by conducting meticulous preprocessing and feature extraction. Following the preparation process, the models were trained using stratified dataset split with a 70/30 ratio. The testing results indicated that the VGG 16 and VGG 19 architectures yielded the highest performance results, as they produced the optimal precision, recall, and F1-score values that reached up to 96.7%, 97.2%, and 96.5%, respectively, along with the highest AUC-ROC scores. In comparison, the CNN and RNN models presented lower performance results in each measured metric. The superiority of the VGG architectures strengthes the idea that the complexity and the capacity of the model to memorize and retain the imaging features is crucial for the accurate detection of the tumors. As such, the improved results can be helpful for healthcare professionals, as they provide powerful tools to ensure precise detection and characteristics of brain tumors. Nevertheless, further research is required to validate the results with a larger dataset, and the adherence of the study to the black-box nature of the model poses limitations on the interpretation. In any case, the study supplements the growing body of research in medical imaging and contributes to the prevention and management of brain tumors.

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“…This makes it difficult to develop an ML model that can accurately detect and classify heart sounds in a wide range of individuals. Heart sounds can be difficult to distinguish from other sounds in the body, such as breathing and blood flow [7]. Additionally, external noise such as background noise or equipment noise can also interfere with the recording and analysis of heart sounds [5].…”

Section: Introductionmentioning

confidence: 99%

Systematic Review for Phonocardiography Classification Based on Machine Learning

Altaf,

Mahdin,

Alive

et al. 2023

IJACSA

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Phonocardiography, the recording and analysis of heart sounds, has become an essential tool in diagnosing cardiovascular diseases (CVDs). In recent years, machine learning and deep learning techniques have dramatically improved the automation of phonocardiogram classification, making it possible to delve deeper into intricate patterns that were previously difficult to discern. Deep learning, in particular, leverages layered neural networks to process data in complex ways, mimicking how the human brain works. This has contributed to more accurate and efficient diagnoses. This systematic review aims to examine the existing literature on phonocardiography classification based on machine learning, focusing on algorithms, datasets, feature extraction methods, and classification models utilized. The materials and methods used in the study involve a comprehensive search of relevant literature and a critical evaluation of the selected studies. The review also discusses the challenges encountered in this field, especially when incorporating deep learning techniques, and suggests future research directions. Key findings indicate the potential of machine and deep learning in enhancing the accuracy of phonocardiography classification, thereby improving cardiovascular disease diagnosis and patient care. The study concludes by summarizing the overall implications and recommendations for further advancements in this area.

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An Analytical Review of Heart Failure Detection based on IoT and Machine Learning

Cited by 12 publications

References 17 publications

A Framework of Digital Twins for Improving Respiratory Health and Healthcare Measures

A Framework of Digital Twins for Improving Respiratory Health and Healthcare Measures

A Comparative analysis of Advanced Machine Learning Techniques for Enhancing Brain Tumor Detection

Systematic Review for Phonocardiography Classification Based on Machine Learning

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