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 - Couvy-Duchesne, Baptiste; Faouzi, Johann; Martin, Benoît; Thibeau–Sutre, Elina; Wild, Adam; Ansart, Manon; Durrleman, Stanley; Dormont, Didier; Burgos, Ninon; Colliot, Olivier (Frontiers in Psychiatry, 2020)
We ranked 3rd in the PAC 2019 challenge, by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1w MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceed the number of observations. In particular, two versions of Best Linear Unbiased Predictor (BLUP), Support Vector Machine (SVM), two shallow Convolutional Neural Networks (CNN), as well as 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, 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 sub-group. Our results may be used to guide researchers to build age predictors on healthy individuals, that can be used in research and in the clinics as non-specific predictors of disease status.