Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis Article Swipe
Sunil L. Bangare
,
Deepali Virmani
,
Girija Rani Karetla
,
Pankaj Chaudhary
,
Harveen Kaur
,
Syed Nisar Hussain Bukhari
,
Shahajan Miah
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/6987569
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/6987569
The term heart-related disease is stated as the range of condition that impacts an individual heart negatively. In the current scenario, cardiovascular diseases are causing more deaths when compared with other ailments, it has been estimated that there are nearly 18 million deaths annually as per the recent report released by World Health Organization (WHO). It has been stated that unhealthy habits and other related aspects adopted by individuals are considered as the primary reasons for an increase in the risk of heart diseases. High cholesterol, eating more junk foods, hypertension, etc., created the issue related to heart diseases. Hence, addressing food quality and suggesting better eating habits enable individuals to enhance their living and support better health. The application of new technologies like machine learning, deep learning, and other models support doctors, nurses, and radiologists to predict heart disease effectively. Studies have stated that the various models are used mainly for the classification and forecasting of the diagnosis of heart-related diseases. The researchers have identified that critical algorithms like CART support the predictability of the disease by 93.3% whereas the conventional models possess vert less specificity. Furthermore, deep neural networks can be applied for analyzing and detecting heart failures effectively and supporting medical practitioners in making better and more critical clinical decisions making. The researchers focus on using a descriptive research study for performing the study; moreover, the researcher collates the data using the questionnaire method, which enables sourcing the critical information from the medical practitioners and supports in making critical data analysis effectively. The researchers also use secondary data modes for sourcing the information related to past studies on the related topic. The researchers use the frequency analysis, correlation analysis, and structural equation model analysis for performing the study, and the results are stated in detail in the respective sections.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/6987569
- https://downloads.hindawi.com/journals/jfq/2022/6987569.pdf
- OA Status
- gold
- Cited By
- 14
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4290725154
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4290725154Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2022/6987569Digital Object Identifier
- Title
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Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model AnalysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-08-08Full publication date if available
- Authors
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Sunil L. Bangare, Deepali Virmani, Girija Rani Karetla, Pankaj Chaudhary, Harveen Kaur, Syed Nisar Hussain Bukhari, Shahajan MiahList of authors in order
- Landing page
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https://doi.org/10.1155/2022/6987569Publisher landing page
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https://downloads.hindawi.com/journals/jfq/2022/6987569.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/jfq/2022/6987569.pdfDirect OA link when available
- Concepts
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Disease, Quality (philosophy), Predictability, Heart disease, Medicine, Artificial intelligence, Psychology, Computer science, Environmental health, Risk analysis (engineering), Pathology, Statistics, Mathematics, Philosophy, EpistemologyTop concepts (fields/topics) attached by OpenAlex
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14Total citation count in OpenAlex
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2025: 1, 2024: 2, 2023: 8, 2022: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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