Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge.

We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceeds the number of observations, in particular, two versions of best linear unbiased predictor (BLUP), support vector machine (SVM), two shallow convolutional neural networks (CNNs), and the famous ResNet and Inception V1. Ensemble learning was derived from estimating weights via linear regression in a hold-out subset of the training sample. We further evaluated and identified factors that could influence prediction accuracy: choice of algorithm, ensemble learning, and features used as input/MRI image processing. Our prediction error was correlated with age, and absolute error was greater for older participants, suggesting to increase the training sample for this subgroup. Our results may be used to guide researchers to build age predictors on healthy individuals, which can be used in research and in the clinics as non-specific predictors of disease status.
Competing Interests: OC reports having received consulting fees from AskBio (2020), fees for writing a lay audience short paper from Expression Santé (2019), and speaker fees for a lay audience presentation from Palais de la découverte (2017) and reports that his laboratory has received grants (paid to the institution) from Air Liquide Medical Systems (2011–2016) and Qynapse (2017–present). The members from his laboratory have co-supervised a Ph.D. thesis with myBrainTechnologies (2016-present). OC's spouse is an employee of myBrainTechnologies (2015–present). OC and SD have submitted a patent to the International Bureau of the World Intellectual Property Organization (PCT/IB2016/0526993, Schiratti J-B, Allassonniere S, OC, SD, a method for determining the temporal progression of a biological phenomenon and associated methods and devices) (2016). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2020 Couvy-Duchesne, Faouzi, Martin, Thibeau–Sutre, Wild, Ansart, Durrleman, Dormont, Burgos and Colliot.)