Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data
Abstract Alzheimer’s disease (AD) is the most common form of dementia. Early and accurate detection of AD is crucial to plan for disease modifying therapies that could prevent or delay the conversion to sever stages of the disease. As a chronic disease, patient’s multivariate time series data includ...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-09-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-42796-6 |
| _version_ | 1797453001811558400 |
|---|---|
| author | Hager Saleh Eslam Amer Tamer Abuhmed Amjad Ali Ala Al-Fuqaha Shaker El-Sappagh |
| author_facet | Hager Saleh Eslam Amer Tamer Abuhmed Amjad Ali Ala Al-Fuqaha Shaker El-Sappagh |
| author_sort | Hager Saleh |
| collection | DOAJ |
| description | Abstract Alzheimer’s disease (AD) is the most common form of dementia. Early and accurate detection of AD is crucial to plan for disease modifying therapies that could prevent or delay the conversion to sever stages of the disease. As a chronic disease, patient’s multivariate time series data including neuroimaging, genetics, cognitive scores, and neuropsychological battery provides a complete profile about patient’s status. This data has been used to build machine learning and deep learning (DL) models for the early detection of the disease. However, these models still have limited performance and are not stable enough to be trusted in real medical settings. Literature shows that DL models outperform classical machine learning models, but ensemble learning has proven to achieve better results than standalone models. This study proposes a novel deep stacking framework which combines multiple DL models to accurately predict AD at an early stage. The study uses long short-term memory (LSTM) models as base models over patient’s multivariate time series data to learn the deep longitudinal features. Each base LSTM classifier has been optimized using the Bayesian optimizer using different feature sets. As a result, the final optimized ensembled model employed heterogeneous base models that are trained on heterogeneous data. The performance of the resulting ensemble model has been explored using a cohort of 685 patients from the University of Washington's National Alzheimer’s Coordinating Center dataset. Compared to the classical machine learning models and base LSTM classifiers, the proposed ensemble model achieves the highest testing results (i.e., 82.02, 82.25, 82.02, and 82.12 for accuracy, precision, recall, and F1-score, respectively). The resulting model enhances the performance of the state-of-the-art literature, and it could be used to build an accurate clinical decision support tool that can assist domain experts for AD progression detection. |
| first_indexed | 2024-03-09T15:16:34Z |
| format | Article |
| id | doaj.art-6adf926f399e4c6bac85f670ae4bb9fd |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:16:34Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-6adf926f399e4c6bac85f670ae4bb9fd2023-11-26T13:03:18ZengNature PortfolioScientific Reports2045-23222023-09-0113112210.1038/s41598-023-42796-6Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series dataHager Saleh0Eslam Amer1Tamer Abuhmed2Amjad Ali3Ala Al-Fuqaha4Shaker El-Sappagh5Faculty of Computers and Artificial Intelligence, South Valley UniversityCommunications and Information Technology, The Institute of Electronics, Queen’s University of BelfastInformation Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan UniversityInformation and Computing Technology (ICT) Division, College of Science and Engineering (CSE), Hamad Bin Khalifa UniversityInformation and Computing Technology (ICT) Division, College of Science and Engineering (CSE), Hamad Bin Khalifa UniversityInformation Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan UniversityAbstract Alzheimer’s disease (AD) is the most common form of dementia. Early and accurate detection of AD is crucial to plan for disease modifying therapies that could prevent or delay the conversion to sever stages of the disease. As a chronic disease, patient’s multivariate time series data including neuroimaging, genetics, cognitive scores, and neuropsychological battery provides a complete profile about patient’s status. This data has been used to build machine learning and deep learning (DL) models for the early detection of the disease. However, these models still have limited performance and are not stable enough to be trusted in real medical settings. Literature shows that DL models outperform classical machine learning models, but ensemble learning has proven to achieve better results than standalone models. This study proposes a novel deep stacking framework which combines multiple DL models to accurately predict AD at an early stage. The study uses long short-term memory (LSTM) models as base models over patient’s multivariate time series data to learn the deep longitudinal features. Each base LSTM classifier has been optimized using the Bayesian optimizer using different feature sets. As a result, the final optimized ensembled model employed heterogeneous base models that are trained on heterogeneous data. The performance of the resulting ensemble model has been explored using a cohort of 685 patients from the University of Washington's National Alzheimer’s Coordinating Center dataset. Compared to the classical machine learning models and base LSTM classifiers, the proposed ensemble model achieves the highest testing results (i.e., 82.02, 82.25, 82.02, and 82.12 for accuracy, precision, recall, and F1-score, respectively). The resulting model enhances the performance of the state-of-the-art literature, and it could be used to build an accurate clinical decision support tool that can assist domain experts for AD progression detection.https://doi.org/10.1038/s41598-023-42796-6 |
| spellingShingle | Hager Saleh Eslam Amer Tamer Abuhmed Amjad Ali Ala Al-Fuqaha Shaker El-Sappagh Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data Scientific Reports |
| title | Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data |
| title_full | Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data |
| title_fullStr | Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data |
| title_full_unstemmed | Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data |
| title_short | Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data |
| title_sort | computer aided progression detection model based on optimized deep lstm ensemble model and the fusion of multivariate time series data |
| url | https://doi.org/10.1038/s41598-023-42796-6 |
| work_keys_str_mv | AT hagersaleh computeraidedprogressiondetectionmodelbasedonoptimizeddeeplstmensemblemodelandthefusionofmultivariatetimeseriesdata AT eslamamer computeraidedprogressiondetectionmodelbasedonoptimizeddeeplstmensemblemodelandthefusionofmultivariatetimeseriesdata AT tamerabuhmed computeraidedprogressiondetectionmodelbasedonoptimizeddeeplstmensemblemodelandthefusionofmultivariatetimeseriesdata AT amjadali computeraidedprogressiondetectionmodelbasedonoptimizeddeeplstmensemblemodelandthefusionofmultivariatetimeseriesdata AT alaalfuqaha computeraidedprogressiondetectionmodelbasedonoptimizeddeeplstmensemblemodelandthefusionofmultivariatetimeseriesdata AT shakerelsappagh computeraidedprogressiondetectionmodelbasedonoptimizeddeeplstmensemblemodelandthefusionofmultivariatetimeseriesdata |