Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study

BMC Medical Research Methodology

Research

Lu Zhang¹  Francisco Azuaje²  Michel Vaillant³  Moses Ngari⁴  Gloria A. Aguayo⁵  Guy Fagherazzi⁵  Magali Perquin⁶  Laetitia Huiart⁶  Valerie Moran⁷  Rejko Krüger⁸  Cyril Ferdynus⁹ 

[1] Bioinformatics Platform, Luxembourg Institute of Health, Strassen, Luxembourg;Bioinformatics Platform, Luxembourg Institute of Health, Strassen, Luxembourg;Genomics England, London, UK;Competence Center for Methodology and Statistics, Translational Medicine Operations Hub, Luxembourg Institute of Health, Strassen, Luxembourg;Competence Center for Methodology and Statistics, Translational Medicine Operations Hub, Luxembourg Institute of Health, Strassen, Luxembourg;KEMRI/Wellcome Trust Research Programme, Kilifi, Kenya;Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg;Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg;Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg;Living Conditions Department, Luxembourg Institute of Socio-Economic Research, Esch-Sur-Alzette, Luxembourg;LCSB, Luxembourg Centre for System Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg;Parkinson Research Clinic, Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg;Transversal Translational Medicine, Luxembourg Institute of Health, Strassen, Luxembourg;Methodological Support Unit, Félix Guyon University Hospital Center, Saint-Denis, La Réunion, France;

关键词: Deep neural networks;    Cox models;    Parkinson disease;    Alzheimer;    Dementia;    Prediction;    Tabular data;    Older general population;   

DOI  :  10.1186/s12874-023-01837-4

 received in 2022-06-14, accepted in 2023-01-06,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundIn the older general population, neurodegenerative diseases (NDs) are associated with increased disability, decreased physical and cognitive function. Detecting risk factors can help implement prevention measures. Using deep neural networks (DNNs), a machine-learning algorithm could be an alternative to Cox regression in tabular datasets with many predictive features. We aimed to compare the performance of different types of DNNs with regularized Cox proportional hazards models to predict NDs in the older general population.MethodsWe performed a longitudinal analysis with participants of the English Longitudinal Study of Ageing. We included men and women with no NDs at baseline, aged 60 years and older, assessed every 2 years from 2004 to 2005 (wave2) to 2016–2017 (wave 8). The features were a set of 91 epidemiological and clinical baseline variables. The outcome was new events of Parkinson’s, Alzheimer or dementia. After applying multiple imputations, we trained three DNN algorithms: Feedforward, TabTransformer, and Dense Convolutional (Densenet). In addition, we trained two algorithms based on Cox models: Elastic Net regularization (CoxEn) and selected features (CoxSf).Results5433 participants were included in wave 2. During follow-up, 12.7% participants developed NDs. Although the five models predicted NDs events, the discriminative ability was superior using TabTransformer (Uno’s C-statistic (coefficient (95% confidence intervals)) 0.757 (0.702, 0.805). TabTransformer showed superior time-dependent balanced accuracy (0.834 (0.779, 0.889)) and specificity (0.855 (0.0.773, 0.909)) than the other models. With the CoxSf (hazard ratio (95% confidence intervals)), age (10.0 (6.9, 14.7)), poor hearing (1.3 (1.1, 1.5)) and weight loss 1.3 (1.1, 1.6)) were associated with a higher DNN risk. In contrast, executive function (0.3 (0.2, 0.6)), memory (0, 0, 0.1)), increased gait speed (0.2, (0.1, 0.4)), vigorous physical activity (0.7, 0.6, 0.9)) and higher BMI (0.4 (0.2, 0.8)) were associated with a lower DNN risk.ConclusionTabTransformer is promising for prediction of NDs with heterogeneous tabular datasets with numerous features. Moreover, it can handle censored data. However, Cox models perform well and are easier to interpret than DNNs. Therefore, they are still a good choice for NDs.

【 授权许可】

CC BY   
© The Author(s) 2023

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