Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis
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 th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sunil L. Bangare [verfasserIn] Deepali Virmani [verfasserIn] Girija Rani Karetla [verfasserIn] Pankaj Chaudhary [verfasserIn] Harveen Kaur [verfasserIn] Syed Nisar Hussain Bukhari [verfasserIn] Shahajan Miah [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Übergeordnetes Werk: |
In: Journal of Food Quality - Hindawi-Wiley, 2017, (2022) |
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| Übergeordnetes Werk: |
year:2022 |
| Links: |
|---|
| DOI / URN: |
10.1155/2022/6987569 |
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| Katalog-ID: |
DOAJ078647797 |
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| 520 | |a 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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10.1155/2022/6987569 doi (DE-627)DOAJ078647797 (DE-599)DOAJ3e5614bcb9a34347936e8767860f3f0e DE-627 ger DE-627 rakwb eng TX341-641 Sunil L. Bangare verfasserin aut Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Nutrition. Foods and food supply Deepali Virmani verfasserin aut Girija Rani Karetla verfasserin aut Pankaj Chaudhary verfasserin aut Harveen Kaur verfasserin aut Syed Nisar Hussain Bukhari verfasserin aut Shahajan Miah verfasserin aut In Journal of Food Quality Hindawi-Wiley, 2017 (2022) (DE-627)478505051 (DE-600)2175284-9 17454557 nnns year:2022 https://doi.org/10.1155/2022/6987569 kostenfrei https://doaj.org/article/3e5614bcb9a34347936e8767860f3f0e kostenfrei http://dx.doi.org/10.1155/2022/6987569 kostenfrei https://doaj.org/toc/1745-4557 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022 |
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10.1155/2022/6987569 doi (DE-627)DOAJ078647797 (DE-599)DOAJ3e5614bcb9a34347936e8767860f3f0e DE-627 ger DE-627 rakwb eng TX341-641 Sunil L. Bangare verfasserin aut Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Nutrition. Foods and food supply Deepali Virmani verfasserin aut Girija Rani Karetla verfasserin aut Pankaj Chaudhary verfasserin aut Harveen Kaur verfasserin aut Syed Nisar Hussain Bukhari verfasserin aut Shahajan Miah verfasserin aut In Journal of Food Quality Hindawi-Wiley, 2017 (2022) (DE-627)478505051 (DE-600)2175284-9 17454557 nnns year:2022 https://doi.org/10.1155/2022/6987569 kostenfrei https://doaj.org/article/3e5614bcb9a34347936e8767860f3f0e kostenfrei http://dx.doi.org/10.1155/2022/6987569 kostenfrei https://doaj.org/toc/1745-4557 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022 |
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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 Analysis |
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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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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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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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