Evolutionary Optimization of Artificial Neural Networks and Tree-Based Ensemble Models for Diagnosing Deep Vein Thrombosis

Machine learning algorithms, particularly artificial neural networks, have shown promise in healthcare for disease classification, including diagnosing conditions like deep vein thrombosis. However, the performance of artificial neural networks in medical diagnosis heavily depends on their architecture and hyperparameter configuration, which presents virtually unlimited variations. This work employs evolutionary algorithms to optimize hyperparameters for three classic feed-forward artificial neural networks of pre-determined depths. The objective is to enhance the diagnostic accuracy of the classic neural networks in classifying deep vein thrombosis using electronic health records sourced from a Norwegian hospital. The work compares the predictive performance of conventional feed-forward artificial neural networks with standard tree-based ensemble methods previously successful in disease prediction on the same dataset. Results indicate that while classic neural networks perform comparably to tree-based methods, they do not surpass them in diagnosing thrombosis on this specific dataset. The efficacy of evolutionary algorithms in tuning hyperparameters is highlighted, emphasizing the importance of choosing the optimization technique to maximize machine learning models' diagnostic accuracy.