Enhancing Deep Vein Thrombosis Diagnosis with Multi-Objective Evolutionary Algorithm and Machine Learning

Deep vein thrombosis is a serious medical condition requiring prompt and accurate diagnosis. The identification of thrombosis presents a challenging task characterized by conflicting objectives. Maintaining a delicate balance between optimizing overall diagnostic accuracy and averting the misclassification of ill patients as healthy is paramount in the diagnostic process. Our earlier works focused on optimizing the disease prediction accuracy in machine learning models by experimenting with different techniques. We employed single-objective optimization to fine-tune the classification threshold. Additionally, we applied a multi-objective evolutionary algorithm for hyperparameter optimization, both independently and in combination with feature reduction. Expanding on our previous works, this work employs a multi-objective evolutionary algorithm that concurrently tunes hyperparameters, reduces features, and adjusts the classification threshold. By addressing the inherent conflicting objectives in thrombosis diagnostics, the proposed approach generates a set of Pareto-optimal solutions, representing a balance between maximizing overall diagnostic accuracy and minimizing false negatives. Experimental results indicate that this approach enhances the outcomes of the deep vein thrombosis diagnosis prediction, effectively navigating the trade-off in competing objectives for improved clinical efficacy.