Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability
Abstract Background Predicting progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) is an utmost open issue in AD-related research. Neuropsychological assessment has proven to be useful in identifying MCI patients who are likely to convert to dementia. However, the large batt...
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| Format: | Article |
| Language: | English |
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BMC
2018-12-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12911-018-0710-y |
| _version_ | 1819065325051707392 |
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| author | Telma Pereira Francisco L. Ferreira Sandra Cardoso Dina Silva Alexandre de Mendonça Manuela Guerreiro Sara C. Madeira for the Alzheimer’s Disease Neuroimaging Initiative |
| author_facet | Telma Pereira Francisco L. Ferreira Sandra Cardoso Dina Silva Alexandre de Mendonça Manuela Guerreiro Sara C. Madeira for the Alzheimer’s Disease Neuroimaging Initiative |
| author_sort | Telma Pereira |
| collection | DOAJ |
| description | Abstract Background Predicting progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) is an utmost open issue in AD-related research. Neuropsychological assessment has proven to be useful in identifying MCI patients who are likely to convert to dementia. However, the large battery of neuropsychological tests (NPTs) performed in clinical practice and the limited number of training examples are challenge to machine learning when learning prognostic models. In this context, it is paramount to pursue approaches that effectively seek for reduced sets of relevant features. Subsets of NPTs from which prognostic models can be learnt should not only be good predictors, but also stable, promoting generalizable and explainable models. Methods We propose a feature selection (FS) ensemble combining stability and predictability to choose the most relevant NPTs for prognostic prediction in AD. First, we combine the outcome of multiple (filter and embedded) FS methods. Then, we use a wrapper-based approach optimizing both stability and predictability to compute the number of selected features. We use two large prospective studies (ADNI and the Portuguese Cognitive Complaints Cohort, CCC) to evaluate the approach and assess the predictive value of a large number of NPTs. Results The best subsets of features include approximately 30 and 20 (from the original 79 and 40) features, for ADNI and CCC data, respectively, yielding stability above 0.89 and 0.95, and AUC above 0.87 and 0.82. Most NPTs learnt using the proposed feature selection ensemble have been identified in the literature as strong predictors of conversion from MCI to AD. Conclusions The FS ensemble approach was able to 1) identify subsets of stable and relevant predictors from a consensus of multiple FS methods using baseline NPTs and 2) learn reliable prognostic models of conversion from MCI to AD using these subsets of features. The machine learning models learnt from these features outperformed the models trained without FS and achieved competitive results when compared to commonly used FS algorithms. Furthermore, the selected features are derived from a consensus of methods thus being more robust, while releasing users from choosing the most appropriate FS method to be used in their classification task. |
| first_indexed | 2024-12-21T15:44:40Z |
| format | Article |
| id | doaj.art-5320cd3cae914e209231eed0e50cdc2d |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-21T15:44:40Z |
| publishDate | 2018-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-5320cd3cae914e209231eed0e50cdc2d2022-12-21T18:58:23ZengBMCBMC Medical Informatics and Decision Making1472-69472018-12-0118112010.1186/s12911-018-0710-yNeuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictabilityTelma Pereira0Francisco L. Ferreira1Sandra Cardoso2Dina Silva3Alexandre de Mendonça4Manuela Guerreiro5Sara C. Madeira6for the Alzheimer’s Disease Neuroimaging InitiativeLASIGE, Faculdade de Ciências, Universidade de LisboaInstituto Superior Técnico, Universidade de LisboaLaboratório de Neurociências, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de LisboaCognitive Neuroscience Research Group, Department of Psychology and Educational Sciences and Centre for Biomedical Research (CBMR), University of AlgarveLaboratório de Neurociências, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de LisboaLaboratório de Neurociências, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de LisboaLASIGE, Faculdade de Ciências, Universidade de LisboaAbstract Background Predicting progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) is an utmost open issue in AD-related research. Neuropsychological assessment has proven to be useful in identifying MCI patients who are likely to convert to dementia. However, the large battery of neuropsychological tests (NPTs) performed in clinical practice and the limited number of training examples are challenge to machine learning when learning prognostic models. In this context, it is paramount to pursue approaches that effectively seek for reduced sets of relevant features. Subsets of NPTs from which prognostic models can be learnt should not only be good predictors, but also stable, promoting generalizable and explainable models. Methods We propose a feature selection (FS) ensemble combining stability and predictability to choose the most relevant NPTs for prognostic prediction in AD. First, we combine the outcome of multiple (filter and embedded) FS methods. Then, we use a wrapper-based approach optimizing both stability and predictability to compute the number of selected features. We use two large prospective studies (ADNI and the Portuguese Cognitive Complaints Cohort, CCC) to evaluate the approach and assess the predictive value of a large number of NPTs. Results The best subsets of features include approximately 30 and 20 (from the original 79 and 40) features, for ADNI and CCC data, respectively, yielding stability above 0.89 and 0.95, and AUC above 0.87 and 0.82. Most NPTs learnt using the proposed feature selection ensemble have been identified in the literature as strong predictors of conversion from MCI to AD. Conclusions The FS ensemble approach was able to 1) identify subsets of stable and relevant predictors from a consensus of multiple FS methods using baseline NPTs and 2) learn reliable prognostic models of conversion from MCI to AD using these subsets of features. The machine learning models learnt from these features outperformed the models trained without FS and achieved competitive results when compared to commonly used FS algorithms. Furthermore, the selected features are derived from a consensus of methods thus being more robust, while releasing users from choosing the most appropriate FS method to be used in their classification task.http://link.springer.com/article/10.1186/s12911-018-0710-yFeature selectionEnsemble learningMild cognitive impairmentAlzheimer’s diseasePrognostic predictionNeuropsychological data |
| spellingShingle | Telma Pereira Francisco L. Ferreira Sandra Cardoso Dina Silva Alexandre de Mendonça Manuela Guerreiro Sara C. Madeira for the Alzheimer’s Disease Neuroimaging Initiative Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability BMC Medical Informatics and Decision Making Feature selection Ensemble learning Mild cognitive impairment Alzheimer’s disease Prognostic prediction Neuropsychological data |
| title | Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability |
| title_full | Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability |
| title_fullStr | Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability |
| title_full_unstemmed | Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability |
| title_short | Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability |
| title_sort | neuropsychological predictors of conversion from mild cognitive impairment to alzheimer s disease a feature selection ensemble combining stability and predictability |
| topic | Feature selection Ensemble learning Mild cognitive impairment Alzheimer’s disease Prognostic prediction Neuropsychological data |
| url | http://link.springer.com/article/10.1186/s12911-018-0710-y |
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