NSICA: Multi-objective imperialist competitive algorithm for feature selection in arrhythmia diagnosis

• Published in: Computers in biology and medicine Vol. 161; p. 107025
• Main Authors: Ayar, Mehdi, Isazadeh, Ayaz, Gharehchopogh, Farhad Soleimanian, Seyedi, MirHojjat
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.07.2023; Elsevier Limited
• Subjects: Accuracy; Algorithms; Arrhythmia; Arrhythmia diagnosis; Bionic engineering; Cardiac arrhythmia; Cardiovascular disease; Classification; Datasets; EKG; Electrocardiogram; Electrocardiography; Electrodes; Evolutionary algorithms; Feature selection; Genetic algorithms; Heart; Imperialist competitive algorithm; Machine learning; Medical diagnosis; Methods; Multi-objective; Multiple objective analysis; Optimization; Sorting algorithms; United Kingdom > UK; India; Feature selection; Multi-objective; Imperialist competitive algorithm; Arrhythmia diagnosis; Bionic engineering; Electrocardiogram
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.107025

This study proposes a multi-objective, non-dominated, imperialist competitive algorithm (NSICA) to solve optimal feature selection problems. The NSICA is a multi-objective and discrete version of the original Imperialist Competitive Algorithm (ICA) that utilizes the competition between colonies and imperialists to solve optimization problems. This study focused on solving challenges such as discretization and elitism by modifying the original operations and using a non-dominated sorting approach. The proposed algorithm is independent of the application, and with customization, it could be employed to solve any feature selection problem. We evaluated the algorithm's efficiency using it as a feature selection system for diagnosing cardiac arrhythmias. The Pareto optimal selected features from NSICA were utilized to classify arrhythmias in binary and multi-class forms based on three essential objectives: accuracy, number of features, and false negativity. We applied NSICA to an ECG-based arrhythmia classification dataset from the UCI machine learning repository. The evaluation results indicate the efficiency of the proposed algorithm compared to other state-of-the-art algorithms.