A novel hybrid supervised machine learning model with metaheuristic optimization algorithm for prediction of driver concentration levels.

The rapid expansion of urban living has led to increased road congestion, posing significant challenges for maintaining driver attention and ensuring road safety. This study investigates advanced machine learning techniques to assess and predict driver focus by analyzing key metrics such as concentration scores, reaction times, and stress management capabilities. A comprehensive evaluation of various machine learning models was conducted, including Logistic Regression (LR), Linear Discriminant Analysis (LDA), Ridge Classifier, Decision Tree (DT), Light Gradient Boosting Machines (LightGBM), Naive Bayes (NB), Support Vector Machines (SVM), Random Forest (RF), AdaBoost, XGBoost, CatBoost, K-Nearest Neighbors (KNN), and Stacking. To optimize performance, a metaheuristic optimization algorithm was employed for precise hyperparameter tuning. The novelty of this research lies in the introduction of a novel hybrid machine learning algorithm, "CatBoost+Stacking." By integrating the strengths of CatBoost within a stacking framework, this new model significantly enhances the accuracy of predicting driver concentration levels. Experimental results demonstrate that the "CatBoost+Stacking" model outperforms existing baseline models, offering a more effective approach for monitoring driver behaviour. These findings provide practical insights for developing proactive road safety strategies and reducing accidents, highlighting the transformative potential of hybrid models in creating safer driving environments.