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Published on: October 11, 2018
A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of
Şevket Ay1, Ekin Ekinci1, Zeynep Garip1
1Computer Engineering Department, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, 54187 Turkey.
Insights
This study enhances heart disease and heart failure prediction using meta-heuristic algorithms for feature selection, achieving significantly improved F-scores up to 99.72% with machine learning models.
Area of Science:
- Cardiovascular Disease Research
- Machine Learning Applications
- Computational Intelligence
Background:
- Accurate prediction of heart disease and heart failure is crucial for timely intervention.
- Traditional machine learning models often struggle with optimal feature selection, impacting predictive accuracy.
- Meta-heuristic algorithms offer powerful optimization capabilities for complex datasets.
Purpose of the Study:
- To develop an enhanced machine learning model for predicting heart disease and heart failure.
- To investigate the efficacy of meta-heuristic algorithms (CS, FPA, WOA, HHO) for feature selection in cardiovascular datasets.
- To improve classification accuracy by identifying the most informative feature subsets.
Main Methods:
- Utilized the Cleveland heart disease and heart failure datasets.
- Applied Cuckoo Search (CS), Flower Pollination Algorithm (FPA), Whale Optimization Algorithm (WOA), and Harris Hawks Optimization (HHO) for feature selection.
- Integrated selected features with various machine learning classifiers including K-Nearest Neighbour (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Random Forest (RF).
Main Results:
- Achieved a heart disease prediction F-score of 99.72% using KNN with FPA-selected features (8 features).
- Achieved a heart failure prediction F-score of 97.45% using KNN with HHO-selected features (5 features).
- Demonstrated significant improvements in prediction performance compared to models using original datasets.
Conclusions:
- Meta-heuristic feature selection substantially enhances the performance of machine learning models for cardiovascular disease prediction.
- The proposed approach effectively identifies critical features, leading to higher classification accuracy.
- This study provides a robust framework for improving diagnostic and prognostic models in cardiology.
Abstract:
This study aims to use a machine learning (ML)-based enhanced diagnosis and survival model to predict heart disease and survival in heart failure by combining the cuckoo search (CS), flower pollination algorithm (FPA), whale optimization algorithm (WOA), and Harris hawks optimization (HHO) algorithms, which are meta-heuristic feature selection algorithms. To achieve this, experiments are conducted on the Cleveland heart disease dataset and the heart failure dataset collected from the Faisalabad Institute of Cardiology published at UCI. CS, FPA, WOA, and HHO algorithms for feature selection are applied for different population sizes and are realized based on the best fitness values. For the original dataset of heart disease, the maximum prediction F-score of 88% is obtained using K-nearest neighbour (KNN) when compared to logistic regression (LR), support vector machine (SVM), Gaussian Naive Bayes (GNB), and random forest (RF). With the proposed approach, the heart disease prediction F-score of 99.72% is obtained using KNN for population sizes 60 with FPA by selecting eight features. For the original dataset of heart failure, the maximum prediction F-score of 70% is obtained using LR and RF compared to SVM, GNB, and KNN. With the proposed approach, the heart failure prediction F-score of 97.45% is obtained using KNN for population sizes 10 with HHO by selecting five features. Experimental findings show that the applied meta-heuristic algorithms with ML algorithms significantly improve prediction performances compared to performances obtained from the original datasets. The motivation of this paper is to select the most critical and informative feature subset through meta-heuristic algorithms to improve classification accuracy.
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