International Conference on Computer and Knowledge Engineering

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Improving ADHD Detection with Cost-Sensitive LightGBM

Authors :

Behnam Yousefimehr¹ Mehdi Ghatee² Ali Heydari³

1- Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran 2- Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran 3- Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran

Keywords :

ADHD،Mental Health،Machine Learning،Cost-Sensitive،LightGBM

Abstract :

Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder requiring timely and accurate diagnosis for effective treatment. In this study, we present a novel approach using a cost-sensitive LightGBM (Light Gradient Boosting Machine) model to enhance the prediction of ADHD. Our key contribution lies in the integration of cost-sensitive learning, which prioritizes minimizing the misclassification costs associated with false negatives and false positives, critical in medical diagnostics. Additionally, we leverage the computational efficiency and speed of LightGBM, a gradient boosting framework known for its high performance in handling complex data structures. Our results demonstrate that the cost-sensitive LightGBM model not only improves predictive accuracy but also significantly reduces the time required for training and inference, making it a viable tool for real-world ADHD screening applications. This study highlights the importance of incorporating cost-sensitive algorithms in medical predictions to achieve a balanced trade-off between accuracy and resource efficiency.