Machine Learning Model-Based Applications for Food Management in Alzheimer’s Using Regression Analysis Approach
Alzheimer’s disease (AD) has become a public health concern due to its misinterpretation with vascular dementia (VD) and mixed dementia Alzheimer’s disease (MXD). Therefore, an accurate differentiation of these diseases is essential for improving the treatment procedure. It has been seen that nutrit...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2022/1519451 |
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| author | Sajadul Hassan Kumhar Prabhakara Rao Kapula Harveen Kaur Radeep R. Krishna Mudasir M Kirmani Vijay Anant Athavale Mohd Wazih Ahmad |
| author_facet | Sajadul Hassan Kumhar Prabhakara Rao Kapula Harveen Kaur Radeep R. Krishna Mudasir M Kirmani Vijay Anant Athavale Mohd Wazih Ahmad |
| author_sort | Sajadul Hassan Kumhar |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) has become a public health concern due to its misinterpretation with vascular dementia (VD) and mixed dementia Alzheimer’s disease (MXD). Therefore, an accurate differentiation of these diseases is essential for improving the treatment procedure. It has been seen that nutrition along with several other factors plays a role in the disease progression. Scientists are trying to find a solution using some machine learning (ML) techniques. The ML algorithms used for this purpose are neural networks, support vector machines, regression and many more. The current research is focused on understanding the extent of the application of machine learning tools in enhancing food management for patients with Alzheimer’s since there is no cure known for the same. A total of 100 patient data have been collected where the patients had AD, VD, and MXD. Their demographic data, dietary intake, Fazekas scores, and Hachinski scores were collected (independent variables) and analysed in IBM SPSS by considering the risk of development of AD, VD, and MXD as dependent variables. The findings showed that age is highly related (p<0.001) to the development of these three diseases and other demographics are not prioritized. Discussion of other available journal articles showed that nutritional intake, Fazekas scores, Hachinski scores, and gender are also indicators for predicting these diseases (p<0.001). Thus, this study concluded that age, gender, diet consumption, and Fazekas and Hachinski scores are important indicators for differentiating AD from other diseases, and ML can be used to create a custom nutrition plan based on the patient’s diet and stage of disease progression. Lastly, future scopes of ML have been explained in this paper. |
| format | Article |
| id | doaj-art-c7929b0d093447c18fb29b7aa627e158 |
| 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-c7929b0d093447c18fb29b7aa627e1582025-02-03T05:53:35ZengWileyJournal of Food Quality1745-45572022-01-01202210.1155/2022/1519451Machine Learning Model-Based Applications for Food Management in Alzheimer’s Using Regression Analysis ApproachSajadul Hassan Kumhar0Prabhakara Rao Kapula1Harveen Kaur2Radeep R. Krishna3Mudasir M Kirmani4Vijay Anant Athavale5Mohd Wazih Ahmad6Department of Computer ScienceDepartment of ECEChitkara University Institute of Engineering and TechnologyDepartment of ECEDepartment of Social ScienceWalchand Institute of TechnologyAdama Science and Technology UniversityAlzheimer’s disease (AD) has become a public health concern due to its misinterpretation with vascular dementia (VD) and mixed dementia Alzheimer’s disease (MXD). Therefore, an accurate differentiation of these diseases is essential for improving the treatment procedure. It has been seen that nutrition along with several other factors plays a role in the disease progression. Scientists are trying to find a solution using some machine learning (ML) techniques. The ML algorithms used for this purpose are neural networks, support vector machines, regression and many more. The current research is focused on understanding the extent of the application of machine learning tools in enhancing food management for patients with Alzheimer’s since there is no cure known for the same. A total of 100 patient data have been collected where the patients had AD, VD, and MXD. Their demographic data, dietary intake, Fazekas scores, and Hachinski scores were collected (independent variables) and analysed in IBM SPSS by considering the risk of development of AD, VD, and MXD as dependent variables. The findings showed that age is highly related (p<0.001) to the development of these three diseases and other demographics are not prioritized. Discussion of other available journal articles showed that nutritional intake, Fazekas scores, Hachinski scores, and gender are also indicators for predicting these diseases (p<0.001). Thus, this study concluded that age, gender, diet consumption, and Fazekas and Hachinski scores are important indicators for differentiating AD from other diseases, and ML can be used to create a custom nutrition plan based on the patient’s diet and stage of disease progression. Lastly, future scopes of ML have been explained in this paper.http://dx.doi.org/10.1155/2022/1519451 |
| spellingShingle | Sajadul Hassan Kumhar Prabhakara Rao Kapula Harveen Kaur Radeep R. Krishna Mudasir M Kirmani Vijay Anant Athavale Mohd Wazih Ahmad Machine Learning Model-Based Applications for Food Management in Alzheimer’s Using Regression Analysis Approach Journal of Food Quality |
| title | Machine Learning Model-Based Applications for Food Management in Alzheimer’s Using Regression Analysis Approach |
| title_full | Machine Learning Model-Based Applications for Food Management in Alzheimer’s Using Regression Analysis Approach |
| title_fullStr | Machine Learning Model-Based Applications for Food Management in Alzheimer’s Using Regression Analysis Approach |
| title_full_unstemmed | Machine Learning Model-Based Applications for Food Management in Alzheimer’s Using Regression Analysis Approach |
| title_short | Machine Learning Model-Based Applications for Food Management in Alzheimer’s Using Regression Analysis Approach |
| title_sort | machine learning model based applications for food management in alzheimer s using regression analysis approach |
| url | http://dx.doi.org/10.1155/2022/1519451 |
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