Enhanced Evolutionary Feature Selection and Ensemble Method for Cardiovascular Disease Prediction

Venugopal, Jothi Prakash; Karthikeyan, N.

doi:10.1007/s12539-021-00430-x

Cited by 32 publications

(16 citation statements)

References 104 publications

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“…The experimental results showed that the ensemble classifier outperformed the other algorithms, obtaining an accuracy of 98.6%. Similarly, Prakash and Karthikeyan [146] developed a heart disease prediction method using feature selection and ensemble learning. However, this study combined the genetic algorithm (GA) and LDA to achieve a robust feature selection model.…”

Section: Ensemble Learning Applications In Recent Literaturementioning

confidence: 99%

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

2022

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Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models. This paper presents a concise overview of ensemble learning, covering the three main ensemble methods: bagging, boosting, and stacking, their early development to the recent state-of-the-art algorithms. The study focuses on the widely used ensemble algorithms, including random forest, adaptive boosting (AdaBoost), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). An attempt is made to concisely cover their mathematical and algorithmic representations, which is lacking in the existing literature and would be beneficial to machine learning researchers and practitioners.

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Section: Ensemble Learning Applications In Recent Literaturementioning

confidence: 99%

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

2022

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“…The proposed work is discussed in this section, which covers several phases such as dimensionality reduction and a DEN‐based classification approach with parameter settings. The proposed method is built around a few main tasks, such as loading raw cancer data, reducing the number of dimensions using the enhanced evolutionary feature selection (EEFS) algorithm, 44 and classifying the data using a deep ensemble, as shown in Figure 1.…”

Section: Proposed Methodologymentioning

confidence: 99%

A deep ensemble network model for classifying and predicting breast cancer

Subramanian

Venugopal

2022

Computational Intelligence

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Breast cancer is one of the leading causes of death among women worldwide. In most cases, the misinterpretation of medical diagnosis plays a vital role in increased fatality rates due to breast cancer. Breast cancer can be diagnosed by classifying tumors. There are two different types of tumors, such as malignant and benign tumors. Identifying the type of tumor is a tedious task, even for experts. Hence, an automated diagnosis is necessary. The role of machine learning in medical diagnosis is eminent as it provides more accurate results in classifying and predicting diseases. In this paper, we propose a deep ensemble network (DEN) method for classifying and predicting breast cancer. This method uses a stacked convolutional neural network, artificial neural network and recurrent neural network as the base classifiers in the ensemble. The random forest algorithm is used as the meta‐learner for providing the final prediction. Experimental results show that the proposed DEN technique outperforms all the existing approaches in terms of accuracy, sensitivity, specificity, F‐score and area under the curve (AUC) measures. The analysis of variance test proves that the proposed DEN model is statistically more significant than the other existing classification models; thus, the proposed approach may aid in the early detection and diagnosis of breast cancer in women, hence aiding in the development of early treatment techniques to increase survival rate.

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“…An approach for combining various categorization techniques to create a potent composite model from the data is ensemble learning. The most often utilised ensemble approaches are Random Subspace, Bagging, and Adaboost [25]. An ensemble inducer can be made using any base classifier method, including decision trees, k-nearest neighbours (k-NN), and other foundation learner algorithms.…”

Section: K-nearest Neighbour(knn) Subspace Algorithmmentioning

confidence: 99%

Detection of cardiac abnormalities from 12-lead ecg using complex wavelet sub-band features

Mondal,

Choudhary,

Rathee

2024

Biomed. Phys. Eng. Express

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[Aim of the study: This research endeavors to optimize cardiac anomaly detection by introducing a method focused on selecting the most effective Daubechis wavelet families. The principal aim is to differentiate between cardiac states that are normal and abnormal by utilising longer electrocardiogram (ECG) signal events based on Apnea ECG dataset. Apnea ECG is often used for the detection of sleep apnea, a sleep disorder characterized by repeated interruptions in breathing during sleep. By using machine learning methods, such as Principal Component Analysis (PCA) and different classifiers, the goal is to improve the precision of cardiac irregularity identification.Used method: In order to extract important statistical and sub-band information from lengthy ECG signal episodes, the study uses a novel method that combines discrete wavelet transform with Principal Component Analysis (PCA) for dimension reduction. The methodology focuses on successfully categorising ECG signals by utilising several classifiers, including multilayer perceptron (MLP) neural network, Ensemble Subspace K-Nearest Neighbour(KNN), and Ensemble Bagged Trees, together with varied Daubechis wavelet families (db2, db3, db4, db5, db6).Brief Description of Results: The results emphasise the importance of the chosen Daubechis wavelet family, db5, and its superiority in ECG representation. The method distinguishes normal and abnormal ECG signals well on the Physionet Apnea ECG database. The Neural Network-based method accurately recognises 100% of healthy signals and 97.8% of problematic ones with 98.6% accuracy.Findings: The Ensemble Subspace K-Nearest Neighbour (KNN) and Ensemble Bagged Trees methods got 87.1% accuracy and 0.89 and 0.87 AOC curve values on this dataset, showing that the method works. Precision values of 0.96, 0.86, and 0.86 for MLP Neural Network, KNN Subspace, and Ensemble Bagged Trees confirm their robustness. These findings suggest that wavelet families and machine learning can improve cardiac abnormality detection and categorization.]

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Enhanced Evolutionary Feature Selection and Ensemble Method for Cardiovascular Disease Prediction

Cited by 32 publications

References 104 publications

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

A deep ensemble network model for classifying and predicting breast cancer

Detection of cardiac abnormalities from 12-lead ecg using complex wavelet sub-band features

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