Ruzicka similarity-based brain EEG clustering for improved intelligent epilepsy diagnosis

• Novel PSO-Ruzicka Similarity clustering technique is developed to enhance EEG classification performance. • The proposed method improves feature selection efficiency and reduces computational time, optimizing classification accuracy. • The PSO-Ruzicka clustering approach achieves high classification accuracy: SVM (93.33%), KNN (63.33%), DT (93.33%), NB (100%), and RF (100%) for epileptic seizure detection. This paper aims to introduce a novel clustering method for electroencephalogram (EEG) based on Ruzicka mathematical similarity and incorporates Particle Swarm Optimization (PSO) to enhance feature selection. Medical datasets often contain both convergent and divergent features, making feature selection a crucial step for accurate disease diagnosis and public health applications. The proposed Ruzicka-based clustering method groups EEG records into non-overlapping subgroups according to a defined similarity metric. Cluster centers are determined using a polynomial-based calculation, after which EEG records are assigned to clusters based on the Ruzicka similarity measure. After clustering the EEG records into highly coherent groups, PSO algorithm is employed to identify the most effective subset of features. This process enhances classification accuracy and contributes to more reliable diagnostic outcomes by combining clustering with feature selection. The selected features are then evaluated using multiple classifiers, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Naive Bayes (NB). Accuracy, recall, f1-score and precision measures are conducted to evaluate the model’s performance. Experimental validation is carried out on the Bonn University EEG dataset. With both RF and NB classifiers, the proposed model has achieved up to 100% accuracy compared to other models. The proposed method can be implemented in medical organizations as a decision-support system to assist healthcare professionals in analyzing EEG patterns. Its integration can enhance the accuracy and efficiency of disease diagnosis, leading to improved patient care.