Bias in machine learning models can be significantly mitigated by careful training: Evidence from neuroimaging studies.

ABSTRACT: Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distributions, races and ethnicities, hospitals, and data acquisition equipment and protocols. In the current study, and in the context of three brain diseases, we provide evidence which suggests that when properly trained, machine learning models can generalize well across diverse conditions and do not necessarily suffer from bias. Specifically, by using multistudy magnetic resonance imaging consortia for diagnosing Alzheimer's disease, schizophrenia, and autism spectrum disorder, we find that well-trained models have a high area-under-the-curve (AUC) on subjects across different subgroups pertaining to attri

SUBMITTER: Wang R 

PROVIDER: S-EPMC9962919 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature