Application of Ensemble Machine Learning for Brain Age Detection

Accurately predicting brain age is crucial for assessing the abnormal aging of individuals, as various neurological diseases are associated with deviations from normal brain agingpatterns. In this study, we leveraged diverse machine learning methodologies grounded in feature engineering to precisely forecast brain age. Our comparative analysis of these models revealed a salient positive influence of feature engineering on overall model performance. Although individual models demonstrated superior performance on the validation set, the occurrence of overfitting was noted. Contrary to anticipated outcomes, the efficacy of ensemble models fell short of expectations, while residual models exhibited superior performance in a multi-stage configuration.

Here is the code link of this project: Link

Recommended citation: 2023 iFLYTEK A.I. Developer Contest