Classification of Multi-Class Cardiovascular Disorders using Ensemble Classifier and Impulsive Domain Analysis

Khan, Misha Urooj; Ali, Syeda Zuriat-e-Zehra; Ishtiaq, Arslan; Habib, Kanwal; Gul, Tooba; Samer, Aqsa

doi:10.1109/majicc53071.2021.9526250

Cited by 9 publications

(17 citation statements)

References 29 publications

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“…Advanced signal processing techniques, such as spectrogram analysis, wavelet analysis, or machine learning algorithms, are employed to identify and characterize Extrahls accurately. By detecting and analyzing Extrahls in heart sound signals, clinicians and researchers can improve their understanding of cardiac pathologies and make informed decisions regarding patient care and treatment strategies [14].…”

Section: E Extrahls Heart Sound Signalmentioning

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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Section: E Extrahls Heart Sound Signalmentioning

confidence: 99%

Systematic Review for Phonocardiography Classification Based on Machine Learning

Altaf,

Mahdin,

Alive

et al. 2023

IJACSA

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“…ML-based algorithms construct the outcome, which is based on various linear and non-linear patterns found in the input risk predictors [ 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 ]. This is a critical feature of AI-driven algorithms that differentiates them from other traditional CVD risk assessments.…”

Section: An Overview Of Artificial Intelligence Applications In Healt...mentioning

confidence: 99%

“…The ability to distinguish between patients with a low risk of CVD and those with a high risk of CVD is a characteristic of an ML-based algorithm [ 127 ]. In comparison to the conventional CVD risk calculators, the ML-based algorithms have provided improved risk categorization in terms of multiclass endpoints [ 119 ]. In addition to this, ML-based algorithms can identify symptomatic and asymptomatic carotid atherosclerotic plaques effectively [ 30 , 128 ].…”

Section: An Overview Of Artificial Intelligence Applications In Healt...mentioning

confidence: 99%

“…Incorporating image-based phenotypes into CVD risk prediction models can enhance conventional risk calculators. Suri et al [ 15 , 118 , 119 , 120 , 121 ] have made an effort in this direction by merging traditional risk indicators with image-based phenotypes based on automated carotid ultrasonography [ 122 ]. This fusion was used for determining the 10-year CVD risk [ 118 , 123 , 124 ].…”

Section: An Overview Of Artificial Intelligence Applications In Healt...mentioning

confidence: 99%

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Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Khanna¹,

Maindarkar

Viswanathan

et al. 2022

Healthcare

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Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

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“…Artificial intelligence (AI) systems are used to do complex tasks in a way similar to how humans solve problems [16,17,18,19]. Machine learning is a branch of AI, which is usually characterized as a machine's ability to replicate intelligent human behavior [20,21,22,23,24]. A generalized ML-FDS is shown in Fig.…”

Section: Machine Learning Based Fall Detectionmentioning

confidence: 99%

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

Ishtiaq

Saeed

Khan

et al. 2022

JAREE

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Falls are a major public health concern among the elderly and the number of gadgets designed to detect them has increased significantly in recent years. This document provides a detailed summary of research done on fall detection systems, with comparisons across different types of studies. Its purpose is to be a resource for doctors and engineers who are planning or conducting field research. Following the examination, datasets, limitations, and future imperatives in fall detection were discussed in detail. The quantity of research using context-aware approaches continues to rise, but there is a new trend toward integrating fall detection into smartphones, as well as the use of artificial intelligence in the detection algorithm. Concerns with real-world performance, usability, and reliability are also highlighted.

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Classification of Multi-Class Cardiovascular Disorders using Ensemble Classifier and Impulsive Domain Analysis

Cited by 9 publications

References 29 publications

Systematic Review for Phonocardiography Classification Based on Machine Learning

Systematic Review for Phonocardiography Classification Based on Machine Learning

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

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