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...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2022/6987569 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832562178270429184 |
|---|---|
| author | Sunil L. Bangare Deepali Virmani Girija Rani Karetla Pankaj Chaudhary Harveen Kaur Syed Nisar Hussain Bukhari Shahajan Miah |
| author_facet | Sunil L. Bangare Deepali Virmani Girija Rani Karetla Pankaj Chaudhary Harveen Kaur Syed Nisar Hussain Bukhari Shahajan Miah |
| author_sort | Sunil L. Bangare |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3e5614bcb9a34347936e8767860f3f0e |
| institution | Kabale University |
| issn | 1745-4557 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-3e5614bcb9a34347936e8767860f3f0e2025-02-03T01:23:11ZengWileyJournal of Food Quality1745-45572022-01-01202210.1155/2022/6987569Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model AnalysisSunil L. Bangare0Deepali Virmani1Girija Rani Karetla2Pankaj Chaudhary3Harveen Kaur4Syed Nisar Hussain Bukhari5Shahajan Miah6Department of Information TechnologyVivekananda Institute of Professional Studies-Technical CampusSchool of Computers Data and Mathematical SciencesGRD Institute of Management & TechnologyChitkara University Institute of Engineering and TechnologyNational Institute of Electronics and Information Technology (NIELIT)Department of EEEThe 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.http://dx.doi.org/10.1155/2022/6987569 |
| spellingShingle | Sunil L. Bangare Deepali Virmani Girija Rani Karetla Pankaj Chaudhary Harveen Kaur Syed Nisar Hussain Bukhari Shahajan Miah Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis Journal of Food Quality |
| title | Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis |
| title_full | Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis |
| title_fullStr | Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis |
| title_full_unstemmed | Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis |
| title_short | Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis |
| title_sort | forecasting the applied deep learning tools in enhancing food quality for heart related diseases effectively a study using structural equation model analysis |
| url | http://dx.doi.org/10.1155/2022/6987569 |
| work_keys_str_mv | AT sunillbangare forecastingtheapplieddeeplearningtoolsinenhancingfoodqualityforheartrelateddiseaseseffectivelyastudyusingstructuralequationmodelanalysis AT deepalivirmani forecastingtheapplieddeeplearningtoolsinenhancingfoodqualityforheartrelateddiseaseseffectivelyastudyusingstructuralequationmodelanalysis AT girijaranikaretla forecastingtheapplieddeeplearningtoolsinenhancingfoodqualityforheartrelateddiseaseseffectivelyastudyusingstructuralequationmodelanalysis AT pankajchaudhary forecastingtheapplieddeeplearningtoolsinenhancingfoodqualityforheartrelateddiseaseseffectivelyastudyusingstructuralequationmodelanalysis AT harveenkaur forecastingtheapplieddeeplearningtoolsinenhancingfoodqualityforheartrelateddiseaseseffectivelyastudyusingstructuralequationmodelanalysis AT syednisarhussainbukhari forecastingtheapplieddeeplearningtoolsinenhancingfoodqualityforheartrelateddiseaseseffectivelyastudyusingstructuralequationmodelanalysis AT shahajanmiah forecastingtheapplieddeeplearningtoolsinenhancingfoodqualityforheartrelateddiseaseseffectivelyastudyusingstructuralequationmodelanalysis |