Frontiers in Psychiatry (Dec 2020)

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

  • Baptiste Couvy-Duchesne,
  • Baptiste Couvy-Duchesne,
  • Baptiste Couvy-Duchesne,
  • Baptiste Couvy-Duchesne,
  • Baptiste Couvy-Duchesne,
  • Baptiste Couvy-Duchesne,
  • Johann Faouzi,
  • Johann Faouzi,
  • Johann Faouzi,
  • Johann Faouzi,
  • Johann Faouzi,
  • Benoît Martin,
  • Benoît Martin,
  • Benoît Martin,
  • Benoît Martin,
  • Benoît Martin,
  • Elina Thibeau–Sutre,
  • Elina Thibeau–Sutre,
  • Elina Thibeau–Sutre,
  • Elina Thibeau–Sutre,
  • Elina Thibeau–Sutre,
  • Adam Wild,
  • Adam Wild,
  • Adam Wild,
  • Adam Wild,
  • Adam Wild,
  • Manon Ansart,
  • Manon Ansart,
  • Manon Ansart,
  • Manon Ansart,
  • Manon Ansart,
  • Stanley Durrleman,
  • Stanley Durrleman,
  • Stanley Durrleman,
  • Stanley Durrleman,
  • Stanley Durrleman,
  • Didier Dormont,
  • Didier Dormont,
  • Didier Dormont,
  • Didier Dormont,
  • Didier Dormont,
  • Didier Dormont,
  • Ninon Burgos,
  • Ninon Burgos,
  • Ninon Burgos,
  • Ninon Burgos,
  • Ninon Burgos,
  • Olivier Colliot,
  • Olivier Colliot,
  • Olivier Colliot,
  • Olivier Colliot,
  • Olivier Colliot

DOI
https://doi.org/10.3389/fpsyt.2020.593336
Journal volume & issue
Vol. 11

Abstract

Read online

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.

Keywords