A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases

AY, Şevket; Ekinci, Ekin; Garip, Zeynep

doi:10.1007/s11227-023-05132-3

Cited by 31 publications

(6 citation statements)

References 104 publications

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“…It has provided outstanding results for optimization problems in domains such as wireless resource allocation and gold price forecasting (outperforming PSO, Grey Wolf Optimization and genetic algorithm 47 , 48 . The study of Ay et al 49 also affirms the creditable performance of WOA compared with cuckoo search (CS), flower pollination algorithm (FPA), and Harris Hawks Optimization (HHO) algorithms, other metaheuristic algorithms. Also, WOA is not widely explored in the heart disease domain; hence, its creditable performance in different fields, simplicity, and excellent output against other metaheuristic methods make it a choice worth selecting for FS in the HD domain that guarantees good results.…”

Section: Introductionmentioning

confidence: 79%

Empirical exploration of whale optimisation algorithm for heart disease prediction

Atimbire,

Appati,

Owusu

2024

Sci Rep

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Heart Diseases have the highest mortality worldwide, necessitating precise predictive models for early risk assessment. Much existing research has focused on improving model accuracy with single datasets, often neglecting the need for comprehensive evaluation metrics and utilization of different datasets in the same domain (heart disease). This research introduces a heart disease risk prediction approach by harnessing the whale optimization algorithm (WOA) for feature selection and implementing a comprehensive evaluation framework. The study leverages five distinct datasets, including the combined dataset comprising the Cleveland, Long Beach VA, Switzerland, and Hungarian heart disease datasets. The others are the Z-AlizadehSani, Framingham, South African, and Cleveland heart datasets. The WOA-guided feature selection identifies optimal features, subsequently integrated into ten classification models. Comprehensive model evaluation reveals significant improvements across critical performance metrics, including accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve. These enhancements consistently outperform state-of-the-art methods using the same dataset, validating the effectiveness of our methodology. The comprehensive evaluation framework provides a robust assessment of the model’s adaptability, underscoring the WOA’s effectiveness in identifying optimal features in multiple datasets in the same domain.

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Section: Introductionmentioning

confidence: 79%

Empirical exploration of whale optimisation algorithm for heart disease prediction

Atimbire,

Appati,

Owusu

2024

Sci Rep

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“…Consequently, fine-tuned hyperparameters of XGBoost and conducted model training using the optimized parameters were proposed to achieve a superior performance enhancement in cardiovascular disease prediction. The work in [84] provided a comparative investigation that integrates machine learning algorithms with meta-heuristic algorithms for feature selection, aiming to enhance the classification capabilities of machine learning algorithms by identifying features that significantly influence accuracy. The findings affirm that the amalgamation of machine learning and meta-heuristic algorithms leads to superior classification accuracy with a reduced number of features.…”

Section: Significance Of Feature Selection In Cardiovascular Disease ...mentioning

confidence: 99%

A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

Naser,

Majeed,

Alsabah

et al. 2024

Algorithms

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Cardiovascular disease is the leading cause of global mortality and responsible for millions of deaths annually. The mortality rate and overall consequences of cardiac disease can be reduced with early disease detection. However, conventional diagnostic methods encounter various challenges, including delayed treatment and misdiagnoses, which can impede the course of treatment and raise healthcare costs. The application of artificial intelligence (AI) techniques, especially machine learning (ML) algorithms, offers a promising pathway to address these challenges. This paper emphasizes the central role of machine learning in cardiac health and focuses on precise cardiovascular disease prediction. In particular, this paper is driven by the urgent need to fully utilize the potential of machine learning to enhance cardiovascular disease prediction. In light of the continued progress in machine learning and the growing public health implications of cardiovascular disease, this paper aims to offer a comprehensive analysis of the topic. This review paper encompasses a wide range of topics, including the types of cardiovascular disease, the significance of machine learning, feature selection, the evaluation of machine learning models, data collection & preprocessing, evaluation metrics for cardiovascular disease prediction, and the recent trends & suggestion for future works. In addition, this paper offers a holistic view of machine learning’s role in cardiovascular disease prediction and public health. We believe that our comprehensive review will contribute significantly to the existing body of knowledge in this essential area.

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“…Linear classifiers obtain satisfactory results on linear data sets (Sivrikaya et al, 2021;Yuan et al, 2012). Perceptron Learning Rule (PLR) (Karakurt et al, 2022;Rosenblatt, 1958), non-kernelized Support Vector Machine (SVM) (Bumin & Ozcalici, 2023;Cortes & Vapnik, 1995), and Logistic Regression (LR) (Ay et al, 2023;Berkson, 1944) are some well-known linear classifiers. The common property of these linear classifiers is that the models constructed by them are a linear combination of features.…”

Section: Preliminarymentioning

confidence: 99%

Parçacık Sürü Optimizasyonu Yoluyla Geliştirilen Doğrusal Bir Sınıflandırıcının Analizi

Aydin

2023

JCS

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Meta-heuristics are high-level approaches developed to discover a heuristic that provides a reasonable solution to many varieties of optimization problems. The classification problems contain a sort of optimization problem. Simply, the objective herein is to reduce the number of misclassified instances. In this paper, the question of whether meta-heuristic methods can be used to construct linear models or not is answered. To this end, Particle Swarm Optimization (PSO) has been engaged to address linear classification problems. The Particle Swarm Classifier (PSC) with a certain objective function has been compared with Support Vector Machine (SVM), Perceptron Learning Rule (PLR), and Logistic Regression (LR) applied to fifteen data sets. The experimental results point out that PSC can compete with the other classifiers, and it turns out to be superior to other classifiers for some binary classification problems. Furthermore, the average classification accuracies of PSC, SVM, LR, and PLR are 80.8%, 80.6%, 80.9%, and 57.7%, respectively. In order to enhance the classification performance of PSC, more advanced objective functions can be developed. Further, the classification accuracy can be boosted more by constructing tighter constraints via another meta-heuristic.

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A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases

Cited by 31 publications

References 104 publications

Empirical exploration of whale optimisation algorithm for heart disease prediction

Empirical exploration of whale optimisation algorithm for heart disease prediction

A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

Parçacık Sürü Optimizasyonu Yoluyla Geliştirilen Doğrusal Bir Sınıflandırıcının Analizi

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