EBPSO: Enhanced binary particle swarm optimization for cardiac disease classification with feature selection

Abstract

Cardiac disease is one of the leading causes of death worldwide, and its early detection and diagnosis can considerably increase the lifespan of patients. An automated expert system for early and accurate diagnosis that offsets human error can be designed using machine intelligence and appropriate pre-processing of data to ensure accuracy. To that end, enhanced binary particle swarm optimization (EBPSO) has been investigated in this paper to enable the definitive classification of cardiac disease with the aid of feature selection. The primary objective of this study is to improve the accuracy of the classification and the convergence speed while employing a lesser number of features. In order to achieve this, a new adaptive inertia weight was introduced to balance the exploitation and exploration capability of binary particle swarm optimization (BPSO). In addition, a novel idea called 'neighbourhood best' is introduced in the velocity update equation to improve the convergence speed. The proposed EBPSO approach was tested over well-known datasets of heart disease, that is, the Cleveland dataset, Hungarian dataset, Switzerland dataset, and Long-Beach-Va datasets. For classification purposes, two well-known classifiers, k-nearest neighbour, and support vector machine were used with the EBPSO. Furthermore, the performance of EBPSO was compared with the traditional BPSO, as well as with BPSO with proposed inertia weight (W a -BPSO) and other PSO variants. It has been observed that the proposed approach EBPSO with KNN shows remarkable efficiency in terms of average classification accuracy and convergence rate. The average classification accuracy for Cleveland dataset is 92.814%, for the Hungarian dataset is 91.97%, for the Switzerland dataset is 91.626% with EBPSO+KNN and for the Long-Beach-Va dataset is 85.253% with EBPSO + SVM. Additionally, the performance of W a -BPSO was compared with other commonly used inertia weights. The results of this study demonstrated that the proposed approach produces better results in terms of both the accuracy of classification as well as the convergence rate with a reduced feature set.

Classification of heart disease dataset with k-NN optimized by pso and gwo algorithms

“…A parameter value is specified in the PSO method to maximize the size of the particles scattered across the search space and the particles wandering the search space. The direction of these particles in the search space is determined not only by their flight route but also by the collective flight path of the flock, just as it is in a flock of birds (Wadhawan & Maini, 2022). The Block Diagram of PSO is shown in Figure 1.…”

Machine learning tuning by diversity oriented firefly metaheuristics for Industry 4.0

“…As it can be seen from the literature review, swarm intelligence has been extensively utilized to address the feature selection problem Bezdan, Zivkovic, Bacanin, Chhabra, and Suresh (2022); Wadhawan and Maini (2022) as well as for hyper‐parameters tuning EL‐Hasnony et al (2022); Jovanovic et al (2022). However, only a few publications are available where swarm intelligence has been applied to both feature selection and hyper‐parameters optimization at the same time, such as approaches proposed by Niu et al (2021) and Algamal et al (2021).…”

“…, task scheduling in the cloud-based environmentsBacanin et al (2019);;Bezdan, Zivkovic, Bacanin, Strumberger, et al (2022);Chhabra et al (2022), tackling problems present in wireless sensors networksBacanin, Arnaut, Zivkovic, et al (2022);, artificial neural networks optimization and tuningBacanin, Antonijevic, Vukobrat, et al (2022);Bacanin, Bezdan, Venkatachalam, Zivkovic, et al (2021);Bacanin, Stoean, Zivkovic, et al (2022);Bacanin, Zivkovic, Al-Turjman, et al (2022);, intrusion detectionZivkovic, Tair, et al (2022), energy and load balance optimization in 5G networks Bacanin et al (2023), MRI classifier optimization for medical diagnostics Bezdan et al (2020); Venkatachalam et al (2021), health care and pollution estimation Bacanin, Sarac, Budimirovic, et al (2022), credit card fraud detection Jovanovic et al (2022), as well as COVID-19 diagnostics and cases forecastingBezdan, Zivkovic, Bacanin, Chhabra, and Suresh (2022);Zivkovic, Bacanin, Venkatachalam, et al (2021);Zivkovic, Jovanovic, et al (2022);Zivkovic, Stoean, Petrovic, et al (2021).As it can be seen from the literature review, swarm intelligence has been extensively utilized to address the feature selection problemBezdan, Zivkovic, Bacanin, Chhabra, and Suresh (2022);Wadhawan and Maini (2022) as well as for hyper-parameters tuning EL-Hasnony et al (2022);Jovanovic et al (2022). However, only a few publications are available where swarm intelligence has been applied to both feature selection and hyper-parameters optimization at the same time, such as approaches proposed byNiu et al (2021) andAlgamal et al (2021).…”

Memory-Based Sand Cat Swarm Optimization for Feature Selection in Medical Diagnosis

“…However, the authors ignored the balance between the exploration and exploitation phases in the binary version of QANA. The study presented in [46] adapted Binary Particle Swarm Optimization (BPSO) for feature selection tasks using the inertia weight operator. This was used to update the velocity of the BPSO while also balancing the exploitation and exploration features of BPSO.…”