ReliefF based feature selection and Gradient Squirrel search Algorithm enabled Deep Maxout Network for detection of heart disease

•Gradient Squirrel Search Algorithm-Deep Maxout Network (GSSA-DMN) based detection of heart disease done.•Here, an input data is obtained from particular database and is passed to data pre-processing.•In data pre-processing, data is transformed to considerable patterns utilizing log scaling and Feat...

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Bibliographic Details
Published in:	Biomedical signal processing and control Vol. 87; p. 105446
Main Authors:	Balasubramaniam, S, Vijesh Joe, C, Manthiramoorthy, Chinnadurai, Satheesh Kumar, K
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.01.2024
Subjects:	Deep Maxout Network (DMN) Gradient descent optimization (GDO) Log scaling ReliefF Squirrel search algorithm (SSA) Gradient descent optimization (GDO) Log scaling Deep Maxout Network (DMN) Squirrel search algorithm (SSA) ReliefF
ISSN:	1746-8094, 1746-8108
Online Access:	Get full text
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Summary:	•Gradient Squirrel Search Algorithm-Deep Maxout Network (GSSA-DMN) based detection of heart disease done.•Here, an input data is obtained from particular database and is passed to data pre-processing.•In data pre-processing, data is transformed to considerable patterns utilizing log scaling and Feature selection is done on pre-processed data employing ReliefF.•Heart disease detection is done by DMN that is trained by GSSA.•GSSA is designed by combining Gradient descent optimization (GDO) with Squirrel search algorithm (SSA).•GSSA-DMN attained high accuracy, sensitivity and specificity values about 93.2%, 93% and 91.5%.•The accuracy of the proposed method is 6.97%, 5.79%, 4.50%, 3.43%, and 1.93% higher than the existing methods, such as, BF-PSO, Bi-LSTM-CRF, XGBoost, RLNNC, and DMOA-SqueezeNet for K-value. Detecting heart disease is challenging in clinical settings, leading to an increase in mortality rates. Current detection processes often rely on Electrocardiography (ECG) signal analysis, which requires accurate data processing and feature extraction. Traditional methods have limitations like processing time and accuracy. To address these issues, a novel approach called Gradient Squirrel Search Algorithm-Deep Maxout Network (GSSA-DMN) is proposed for more effective heart disease detection. The proposed GSSA-DMN approach involves several steps. Initially, input data is obtained from a specific database and subjected to data pre-processing, including log scaling for pattern transformation. Feature selection is then performed using ReliefF on the pre-processed data. The core of the approach lies in the Deep Maxout Network (DMN) trained by the Gradient Squirrel Search Algorithm (GSSA), which combines Gradient Descent Optimization (GDO) with the Squirrel Search Algorithm (SSA). The GSSA-DMN approach demonstrates remarkable performance. It achieves high accuracy, sensitivity, and specificity values of approximately 93.2%, 93%, and 91.5%, respectively. These results indicate its effectiveness in heart disease detection. Comparatively, the proposed GSSA-DMN method outperforms existing techniques. Its accuracy surpasses those of other methods by margins of 6.97%, 5.79%, 4.50%, 3.43%, and 1.93% when compared to BF-PSO, Bi-LSTM-CRF, XGBoost, RLNNC, and DMOA-SqueezeNet for K-value. This suggests that GSSA-DMN provides superior accuracy in detecting heart disease. In summary, the GSSA-DMN approach presents a promising solution for improving the accuracy and efficiency of heart disease detection compared to traditional methods and existing state-of-the-art techniques.
ISSN:	1746-8094 1746-8108
DOI:	10.1016/j.bspc.2023.105446