Feature selection using Artificial Bee Colony for cardiovascular disease classification | Semantic Scholar (2024)

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Topics

Classification (opens in a new tab)Support Vector Machines (opens in a new tab)Classification Accuracy (opens in a new tab)Feature Selection (opens in a new tab)Artificial Bee Colony (opens in a new tab)Feature Subsets (opens in a new tab)Cleveland Heart Disease Dataset (opens in a new tab)Computation Complexity (opens in a new tab)Swarm Intelligence (opens in a new tab)Machine Learning (opens in a new tab)

53 Citations

A Novel Feature Selection Algorithm for Heart Disease Classification
    B. Subanya

    Computer Science, Medicine

  • 2015

A metaheuristic algorithm is used to determine the optimal feature subset with improved classification accuracy in heart disease diagnosis using a Binary Artificial Bee Colony (BABC) algorithm and results indicate that, BABC–KNN outperform the other methods.

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Artificial Bee Colony based Feature Selection for Effective Cardiovascular Disease Diagnosis
    R. Rajalaxmi

    Medicine, Computer Science

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An effective algorithm that can remove irrelevant dimensions from large data and to predict more accurately the presence of disease is designed and results indicate that, BABC–Naive Bayesian outperform the other methods.

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Feature Selection Method Based on Grey Wolf Optimization for Coronary Artery Disease Classification
    Qasem Al-TashiH. RaisS. Jadid

    Medicine, Computer Science

    Advances in Intelligent Systems and Computing

  • 2018

A novel wrapper feature selection method is proposed to determine the optimal feature subset for diagnosing coronary artery disease and outperforms current existing approaches with an achievement of 89.83% in accuracy, 93% in sensitivity and 91% in specificity rates.

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Feature Selection Method using Genetic Algorithm for Medical Dataset

A wrapper-based feature selection using the Genetic Algorithm (GA) is proposed and the classifier is based on Support Vector Machine (SVM) and the results obtained yielded competitive results on most of the datasets.

Enhanced Model for Prediction and Classification of Cardiovascular Disease using Decision Tree with Particle Swarm Optimization
    P. DeepikaS. Sasikala

    Medicine, Computer Science

    2020 4th International Conference on Electronics…

  • 2020

For making enhanced predictions and classification in Cardio Vascular Disease, the data mining model is proposed with the J48 algorithm with Particle Swarm Optimization (PSO) and the experimental results highlight the performance efficiency in the CardioVascular Disease prediction and classification.

  • 9
Hybrid Swarm Intelligence Algorithms with Ensemble Machine Learning for Medical Diagnosis
    Qasem Al-TashiH. RaisS. J. Abdulkadir

    Medicine, Computer Science

    2018 4th International Conference on Computer and…

  • 2018

A hybrid Dynamic ant colony system three update levels, with wavelets transform, and singular value decomposition integrating support vector machine is proposed, seeking to minimize subset of features to attain a satisfactory disease diagnosis on a wide range of diseases with the highest accuracy, sensitivity, and specificity.

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Heart Disease Prediction Based on an Optimal Feature Selection Method using Autoencoder
    M. AzharPrincy Ann Thomas

    Medicine, Computer Science

  • 2020

This work has used an artificial neural network-based autoencoder for effective feature selection and aims at finding optimal features by applying machine learning techniques resulting in improving the performance in the prediction of cardiovascular disease.

Prediction of Cardiovascular Disease using Machine Learning
    M.BalakrishnanA. ChristopherP.RamprakashA.Logeswari

    Medicine, Computer Science

  • 2021

Here prediction model is developed using Random Forest classification technique - Method for classification, regression by constructing a multitude of decision trees at training time, and Avoids over fitting can deal with large number of features.

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A hybrid GA and PSO optimized approach for heart-disease prediction based on random forest
    Mohamed G. El-ShafieyAhmed M. HagagE. El-DahshanManal A. Ismail

    Medicine, Computer Science

    Multimedia Tools and Applications

  • 2022

A hybrid genetic algorithm (GA) and particle swarm optimization (PSO) optimized approach based on random forest (RF) is developed and used to select the optimal features that can increase the accuracy of heart-disease prediction.

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A Novel Approach for the Classification of Leukemia Using Artificial Bee Colony Optimization Technique and Back-Propagation Neural Networks
    Rudrani SharmaRakesh Kumar

    Medicine, Computer Science

    Proceedings of 2nd International Conference on…

  • 2018

Simulation results show that the PCA based ABC-BPNN approach gives better results than Genetic-based BPNN algorithm in terms of FAR, FRR, and accuracy.

  • 10

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With the categorizing framework, the efforts toward-building an integrated system for intelligent feature selection are continued, and an illustrative example is presented to show how existing feature selection algorithms can be integrated into a meta algorithm that can take advantage of individual algorithms.

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Large-scale attribute selection using wrappers
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This work investigates Linear Forward Selection, a technique to reduce the number of attributes expansions in each forward selection step and shows that this approach is faster, finds smaller subsets and can even increase the accuracy compared to standard forward selection.

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