Design and Development of a Hybrid Evolutionary Method with a Special Selection Artificial Immune System for Stroke Prediction: A Balancing Approach

Abstract

A stroke is a serious neurological condition that occurs due to either blockages or bleeding in the brain, which can lead to death or long-term disability. This study aims to enhance the accuracy of disease diagnosis in imbalanced stroke patient datasets. The model incorporates an artificial immune system algorithm, whose parameters are fine-tuned using the Firefly algorithm to ensure both stability and balanced data. To enhance the performance for the underrepresented class, the One-Sided Selection method is employed. The model’s effectiveness was tested in two separate experiments: one utilizing all available features and the other applying the Artificial Bee Colony (ABC) algorithm to select the most relevant features. The models were trained using six different classification algorithms: CatBoost, Light Gradient Boosting Machine (LightGBM), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Logistic Regression (LR). The results were presented using performance metrics. When trained using all features, the model achieved an accuracy of 93%, specificity of 93%, and sensitivity of 80%. When trained using the best features selected by the ABC algorithm, the model achieved an accuracy of 93%, specificity of 93%, and sensitivity of 82%. Compared to previous studies, the proposed model was effective in both experiments.