Machine Learning-Based Models for Identifying Learning Disabilities

Timely and accurate identification of learning disability (LD) severity is critical for early screening and for guiding appropriate clinical and educational interventions. This study developed a machine learning model with feature selection and hyperparameter optimization (MLFSHO) architecture to predict the severity of LD using heterogeneous clinical data with clinical expert labeling. Four machine learning models including eXtreme Gradient Boosting (XGB), Categorical Boosting (CAT), Light Gradient Boosting Machine (LGBM), and Multi-Layer Perceptron (MLP) were implemented within the MLFSHO architecture that integrates HSIC-based feature selection and Optuna-based joint optimization of feature-related parameters and model hyperparameters. Experiment results indicated all machine learning-based (ML-based) models can provide average accuracy of more than 85%. In addition, hyperparameter optimization consistently improved most predictive performance. Joint optimization of feature-related parameters and model hyperparameters achieved the best overall performance across models. These findings suggest that treating feature selection and hyperparameter tuning as a unified optimization problem can improve the reliability of severity prediction in learning disabilities and support early screening in clinical settings. The proposed MLFSHO architecture provides a systematic approach for modeling heterogeneous clinical data and improves the performance of LD severity prediction.