On a Survival Gradient Boosting, Neural Network and Cox PH Based Approach to Predicting Dementia Diagnosis Risk on ADNI

Musto, Henry; Stamate, Daniel; Logofatu, Doina and Ouarbya, Lahcen. 2024. 'On a Survival Gradient Boosting, Neural Network and Cox PH Based Approach to Predicting Dementia Diagnosis Risk on ADNI'. In: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Istanbul, Turkey 5 - 8 December 2023. [Conference or Workshop Item]

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Abstract or Description

In recent years, attention within the clinical prediction community has turned to the use of survival machine learning as a tool for predicting the risk of developing a disease as a function of time. The current work seeks to contribute to existing literature which demonstrates the utility of these methods when applied to a dementia prediction context. We use the Alzheimer's Disease Neuroimaging Initiative ADNI dataset and model deterioration within two distinct groups, those deemed cognitively normal and those with a formal diagnosis of Mild Cognitive Impairment. In agreement with existing literature we find that survival machine learning outperforms standard survival analysis methods such as Cox PH model, and has very good predictive ability. We propose an innovative approach to predicting dementia diagnosis risk on ADNI, which explores the use of survival neural network and survival extreme gradient boosting techniques that have hitherto seldom been applied to this context. The stability of our models was investigated within a Monte Carlo simulation framework.

Item Type:

Conference or Workshop Item (Paper)

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Dementia Risk Prediction, Survival Analysis, Survival Machine Learning, Gradient Boosting, Neural Networks, Clinical Prediction Modelling, Monte Carlo

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16 October 2023Accepted
5 February 2024Published

Event Location:

Istanbul, Turkey

Date range:

5 - 8 December 2023

Item ID:


Date Deposited:

15 Apr 2024 09:57

Last Modified:

15 Apr 2024 15:52



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