Understanding alzheimer's disease diagnostic factors through machine learning
Alzheimer’s disease (AD) is one of the leading public health concerns that continues to grow as the world’s population rapidly ages. It is therefore crucial to understand factors behind AD diagnosis and patient classification, where one of the leading perspectives in research today is the A/T/N fram...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/177801 |
| _version_ | 1811678958712782848 |
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| author | Wong, Lisa Maria Qi Qing |
| author2 | Yu Junhong |
| author_facet | Yu Junhong Wong, Lisa Maria Qi Qing |
| author_sort | Wong, Lisa Maria Qi Qing |
| collection | NTU |
| description | Alzheimer’s disease (AD) is one of the leading public health concerns that continues to grow as the world’s population rapidly ages. It is therefore crucial to understand factors behind AD diagnosis and patient classification, where one of the leading perspectives in research today is the A/T/N framework. This is unsuitable for diagnostic implementation due to its arbitrariness and exclusion of other non-biological AD pathologies. Thus, the present study aims to examine other factors contributing to AD risk, such as demographics, cognitive and neuroimaging to provide a holistic perspective on AD diagnosis and risk.
Support vector machine (SVM) modeling was used to train 6 different models in total, resulting in 1 combined model, and separate models for demographic, cognitive assessments, A/T/N, biological factors and structural connectivity as predictors respectively. The models were trained and tested, following which they were evaluated on their performance metrics.
The model which performed the poorest used structural connectivity data as the only predictor, while the cognitive assessments model was the best. The combined factors model did not perform as well as expected, due to integration and dimensionality issues.
This paper supports the idea that biological factors alone are not sufficient to make a proper diagnosis of AD, and that cognitive assessment is a key part of AD evaluation and diagnosis. It also aims to provide the basis for the development of a tool that can be used to aid classification in research settings. |
| first_indexed | 2024-10-01T03:01:32Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/177801 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:01:32Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1778012024-06-02T15:32:13Z Understanding alzheimer's disease diagnostic factors through machine learning Wong, Lisa Maria Qi Qing Yu Junhong School of Social Sciences junhong.yu@ntu.edu.sg Social Sciences Alzheimer’s disease (AD) is one of the leading public health concerns that continues to grow as the world’s population rapidly ages. It is therefore crucial to understand factors behind AD diagnosis and patient classification, where one of the leading perspectives in research today is the A/T/N framework. This is unsuitable for diagnostic implementation due to its arbitrariness and exclusion of other non-biological AD pathologies. Thus, the present study aims to examine other factors contributing to AD risk, such as demographics, cognitive and neuroimaging to provide a holistic perspective on AD diagnosis and risk. Support vector machine (SVM) modeling was used to train 6 different models in total, resulting in 1 combined model, and separate models for demographic, cognitive assessments, A/T/N, biological factors and structural connectivity as predictors respectively. The models were trained and tested, following which they were evaluated on their performance metrics. The model which performed the poorest used structural connectivity data as the only predictor, while the cognitive assessments model was the best. The combined factors model did not perform as well as expected, due to integration and dimensionality issues. This paper supports the idea that biological factors alone are not sufficient to make a proper diagnosis of AD, and that cognitive assessment is a key part of AD evaluation and diagnosis. It also aims to provide the basis for the development of a tool that can be used to aid classification in research settings. Bachelor's degree 2024-05-31T07:09:51Z 2024-05-31T07:09:51Z 2024 Final Year Project (FYP) Wong, L. M. Q. Q. (2024). Understanding alzheimer's disease diagnostic factors through machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177801 https://hdl.handle.net/10356/177801 en application/pdf Nanyang Technological University |
| spellingShingle | Social Sciences Wong, Lisa Maria Qi Qing Understanding alzheimer's disease diagnostic factors through machine learning |
| title | Understanding alzheimer's disease diagnostic factors through machine learning |
| title_full | Understanding alzheimer's disease diagnostic factors through machine learning |
| title_fullStr | Understanding alzheimer's disease diagnostic factors through machine learning |
| title_full_unstemmed | Understanding alzheimer's disease diagnostic factors through machine learning |
| title_short | Understanding alzheimer's disease diagnostic factors through machine learning |
| title_sort | understanding alzheimer s disease diagnostic factors through machine learning |
| topic | Social Sciences |
| url | https://hdl.handle.net/10356/177801 |
| work_keys_str_mv | AT wonglisamariaqiqing understandingalzheimersdiseasediagnosticfactorsthroughmachinelearning |