Research Paper Volume 8, Issue 5 pp 1021—1030
Deep biomarkers of human aging: Application of deep neural networks to biomarker development
Figure 3. DNNs outperform baseline ML approaches in terms of R2 statistics. DNN were compared with 7 ML techniques: GBM (Gradient Boosting Machine), RF (Random Forests), DT (Decision Trees), LR (Linear Regression), kNN (k-Nearest Neighbors), ElasticNet, SVM (Support Vector Machines). (A) GBM shows the higher 0,72 R2 among ML models for biological age prediction. (B) All ML models have comparable high R2 for biological sex prediction.