Abstract
Dementia is one of the most feared illnesses that has a growing year-to-year negative global impact, having a health and social care cost higher than cancer, stroke and chronic heart disease, taken together. Without the availability of a cure, nor a standardised clinical test, the utilisation of machine learning methods to identify individuals that are at risk of developing dementia could bring a new step towards proactive intervention. This study's goal is to carry out a precursor analysis leading to building classification models with enhanced capabilities for differentiating diagnoses of CN (Cognitively Normal), MCI (Mild Cognitive Impairment) and Dementia. The predictive modelling approach we propose is based on the ReliefF method combined with statistical permutation tests for feature selection, and on model training, tuning, and testing based on algorithms such as Random Forests, Support Vector Machines, Gaussian Processes, Stochastic Gradient Boosting, and eXtreme Gradient Boosting. Stability of model performances were studied in computationally intensive Monte Carlo simulations. The results consistently show that our models accurately detect dementia, and also mild cognitive impairment patients by only using the inclusion of baseline measurements as predictors, thus illustrating the importance of baseline measurements. The best results issued from Monte Carlo were achieved by eXtreme Gradient Boosting optimised models, with an accuracy of 0.88 (SD 0.02), a sensitivity of 0.93 (SD 0.02) and a specificity of 0.94 (SD 0.01) for dementia, and a sensitivity of 0.86 (SD 0.02) and a specificity of 0.9 (SD 0.02) for mild cognitive impairment. These results support in particular future developments for a risk-based method that can identify an individual's risk of developing dementia.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 |
| Editors | M. Arif Wani, Mehmed Kantardzic, Moamar Sayed-Mouchaweh, Joao Gama, Edwin Lughofer |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 671-678 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781538668054 |
| ISBN (Print) | 9781538668061 |
| DOIs | |
| Publication status | Published - 17 Jan 2019 |
| Event | 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 - Orlando, United States Duration: 17 Dec 2018 → 20 Dec 2018 |
Conference
| Conference | 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 |
|---|---|
| Country | United States |
| City | Orlando |
| Period | 17/12/18 → 20/12/18 |
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A Machine Learning Framework for Predicting Dementia and Mild Cognitive Impairment. / Stamate, Daniel; Alghambdi, Wajdi; Ogg, Jeremy; Hoile, Richard; Murtagh, Fionn.
Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018. ed. / M. Arif Wani; Mehmed Kantardzic; Moamar Sayed-Mouchaweh; Joao Gama; Edwin Lughofer. Institute of Electrical and Electronics Engineers Inc., 2019. p. 671-678.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A Machine Learning Framework for Predicting Dementia and Mild Cognitive Impairment
AU - Stamate, Daniel
AU - Alghambdi, Wajdi
AU - Ogg, Jeremy
AU - Hoile, Richard
AU - Murtagh, Fionn
PY - 2019/1/17
Y1 - 2019/1/17
N2 - Dementia is one of the most feared illnesses that has a growing year-to-year negative global impact, having a health and social care cost higher than cancer, stroke and chronic heart disease, taken together. Without the availability of a cure, nor a standardised clinical test, the utilisation of machine learning methods to identify individuals that are at risk of developing dementia could bring a new step towards proactive intervention. This study's goal is to carry out a precursor analysis leading to building classification models with enhanced capabilities for differentiating diagnoses of CN (Cognitively Normal), MCI (Mild Cognitive Impairment) and Dementia. The predictive modelling approach we propose is based on the ReliefF method combined with statistical permutation tests for feature selection, and on model training, tuning, and testing based on algorithms such as Random Forests, Support Vector Machines, Gaussian Processes, Stochastic Gradient Boosting, and eXtreme Gradient Boosting. Stability of model performances were studied in computationally intensive Monte Carlo simulations. The results consistently show that our models accurately detect dementia, and also mild cognitive impairment patients by only using the inclusion of baseline measurements as predictors, thus illustrating the importance of baseline measurements. The best results issued from Monte Carlo were achieved by eXtreme Gradient Boosting optimised models, with an accuracy of 0.88 (SD 0.02), a sensitivity of 0.93 (SD 0.02) and a specificity of 0.94 (SD 0.01) for dementia, and a sensitivity of 0.86 (SD 0.02) and a specificity of 0.9 (SD 0.02) for mild cognitive impairment. These results support in particular future developments for a risk-based method that can identify an individual's risk of developing dementia.
AB - Dementia is one of the most feared illnesses that has a growing year-to-year negative global impact, having a health and social care cost higher than cancer, stroke and chronic heart disease, taken together. Without the availability of a cure, nor a standardised clinical test, the utilisation of machine learning methods to identify individuals that are at risk of developing dementia could bring a new step towards proactive intervention. This study's goal is to carry out a precursor analysis leading to building classification models with enhanced capabilities for differentiating diagnoses of CN (Cognitively Normal), MCI (Mild Cognitive Impairment) and Dementia. The predictive modelling approach we propose is based on the ReliefF method combined with statistical permutation tests for feature selection, and on model training, tuning, and testing based on algorithms such as Random Forests, Support Vector Machines, Gaussian Processes, Stochastic Gradient Boosting, and eXtreme Gradient Boosting. Stability of model performances were studied in computationally intensive Monte Carlo simulations. The results consistently show that our models accurately detect dementia, and also mild cognitive impairment patients by only using the inclusion of baseline measurements as predictors, thus illustrating the importance of baseline measurements. The best results issued from Monte Carlo were achieved by eXtreme Gradient Boosting optimised models, with an accuracy of 0.88 (SD 0.02), a sensitivity of 0.93 (SD 0.02) and a specificity of 0.94 (SD 0.01) for dementia, and a sensitivity of 0.86 (SD 0.02) and a specificity of 0.9 (SD 0.02) for mild cognitive impairment. These results support in particular future developments for a risk-based method that can identify an individual's risk of developing dementia.
KW - Dementia
KW - eXtreme Gradient Boosting
KW - Gaussian Processes
KW - Machine Learning
KW - Monte Carlo Simulations
KW - ReliefF
KW - Statistical Permutation Tests
KW - Stochastic Gradient Boosting
KW - Support Vector Machines
UR - http://www.scopus.com/inward/record.url?scp=85062234612&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2018.00107
DO - 10.1109/ICMLA.2018.00107
M3 - Conference contribution
SN - 9781538668061
SP - 671
EP - 678
BT - Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
A2 - Wani, M. Arif
A2 - Kantardzic, Mehmed
A2 - Sayed-Mouchaweh, Moamar
A2 - Gama, Joao
A2 - Lughofer, Edwin
PB - Institute of Electrical and Electronics Engineers Inc.
ER -