Feature selection for medical product demand forecasting with exogeneous variables using Google trend
In an ever-ageing world, the need for enhanced healthcare services has become a paramount global challenge, requiring efficient supply chain management to streamline resource and material management and reduce costs. In healthcare supply chain management, the ability to predict demand is a critical...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166059 |
| _version_ | 1826123533946716160 |
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| author | Koh, Tzi Yong |
| author2 | Jagath C Rajapakse |
| author_facet | Jagath C Rajapakse Koh, Tzi Yong |
| author_sort | Koh, Tzi Yong |
| collection | NTU |
| description | In an ever-ageing world, the need for enhanced healthcare services has become a paramount global challenge, requiring efficient supply chain management to streamline resource and material management and reduce costs. In healthcare supply chain management, the ability to predict demand is a critical factor, and having a reliable forecasting model is essential for informed decision-making. To this end, we propose a unique approach for forecasting the demand for medical products through the analysis of disease-related keyword trends over time, utilizing data from Google Trends. The results demonstrate that incorporating feature selection methods to select important keywords from Google Trends data can significantly improve the accuracy of forecasting models compared to univariate models. Among the various feature selection techniques tested, Pearson's correlation demonstrated the highest level of effectiveness for the set of external covariates analyzed. |
| first_indexed | 2024-10-01T06:06:14Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166059 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:06:14Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1660592023-04-21T15:38:02Z Feature selection for medical product demand forecasting with exogeneous variables using Google trend Koh, Tzi Yong Jagath C Rajapakse School of Computer Science and Engineering ASJagath@ntu.edu.sg Engineering::Computer science and engineering In an ever-ageing world, the need for enhanced healthcare services has become a paramount global challenge, requiring efficient supply chain management to streamline resource and material management and reduce costs. In healthcare supply chain management, the ability to predict demand is a critical factor, and having a reliable forecasting model is essential for informed decision-making. To this end, we propose a unique approach for forecasting the demand for medical products through the analysis of disease-related keyword trends over time, utilizing data from Google Trends. The results demonstrate that incorporating feature selection methods to select important keywords from Google Trends data can significantly improve the accuracy of forecasting models compared to univariate models. Among the various feature selection techniques tested, Pearson's correlation demonstrated the highest level of effectiveness for the set of external covariates analyzed. Bachelor of Engineering (Computer Science) 2023-04-16T03:11:41Z 2023-04-16T03:11:41Z 2023 Final Year Project (FYP) Koh, T. Y. (2023). Feature selection for medical product demand forecasting with exogeneous variables using Google trend. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166059 https://hdl.handle.net/10356/166059 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Koh, Tzi Yong Feature selection for medical product demand forecasting with exogeneous variables using Google trend |
| title | Feature selection for medical product demand forecasting with exogeneous variables using Google trend |
| title_full | Feature selection for medical product demand forecasting with exogeneous variables using Google trend |
| title_fullStr | Feature selection for medical product demand forecasting with exogeneous variables using Google trend |
| title_full_unstemmed | Feature selection for medical product demand forecasting with exogeneous variables using Google trend |
| title_short | Feature selection for medical product demand forecasting with exogeneous variables using Google trend |
| title_sort | feature selection for medical product demand forecasting with exogeneous variables using google trend |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/166059 |
| work_keys_str_mv | AT kohtziyong featureselectionformedicalproductdemandforecastingwithexogeneousvariablesusinggoogletrend |