Comparison of various machine learning regression models based on Human age prediction

Abstract

The development of machine learning strategies has made it possible to diagnose some disease automatically based on data obtained from medical imaging. Brain age is one of the factors that can be used as an indicator of cognitive well-being. Recent advancements in machine learning have made it possible for computers to anticipate classification and prediction outcomes more accurately than humans. In this study, five widely used machine learning regression models (Linear support vector regression (L-SVR), radial basis function support vector regression (RBF-SVR), relevance vector regression (RVR), Elastic Net and Gaussian process regression (GPR)) were trained and evaluated to predict brain age using volumes of brain regions data. Moreover, a dimensionality reduction technique was utilized to reduce the dimensionality of the input feature space. The data were collected from one hundred and eleven participants. The results showed no performance difference amongst models trained on the same type of data, suggesting that the type of input data had a stronger influence on prediction performance than the model choice. The experimental results indicated that the GPR was the best fit model (R2=0.57, R=0.75) among the other regression models while the G-SVR was the worst fit model (R2=0.0006, R=0.025) with such number of the input data.

Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information

“…T1-weighted structural brain MRI data have been used to train and validate a deep convolutional neural network (CNN) architecture [8]. Region-of-interest (ROI) volumes extracted from T1-weighted structural data have been used as features for ML regression models [9]. T1-weighted brain MRI and computed tomography (CT) data were used in a past study [10].…”