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    Area of Science:

    • Machine Learning
    • Statistical Modeling
    • Neuroimaging

    Background:

    • Machine learning regression models frequently exhibit systematic bias, particularly for outcome values distant from the mean.
    • This bias results in underestimation of large values and overestimation of small values, termed 'systematic bias of machine learning regression'.
    • This prediction bias is prevalent across diverse machine learning regression models.

    Purpose of the Study:

    • To demonstrate the persistence of systematic bias in machine learning regression models.
    • To investigate the theoretical foundations of this systematic prediction bias.
    • To propose and validate a novel constrained optimization approach for bias correction.

    Main Methods:

    • Theoretical analysis of the 'systematic bias of machine learning regression'.
    • Development of a general constrained optimization framework for bias correction.
    • Design of computationally efficient algorithms for implementing the correction method.
    • Validation through simulations and application to neuroimaging data for brain age prediction.

    Main Results:

    • Simulation results confirm the proposed correction method effectively eliminates systematic bias in predicted outcomes.
    • The approach successfully addresses the 'systematic bias of machine learning regression' in brain age prediction.
    • Compared to existing models, the proposed method yields unbiased brain age predictions from neuroimaging data.

    Conclusions:

    • A novel constrained optimization approach effectively corrects systematic bias in machine learning regression.
    • The method provides unbiased predictions, particularly crucial in applications like neuroimaging-based brain age estimation.
    • This work offers a significant advancement in improving the accuracy and reliability of machine learning regression models.