Detection and Classification of Myocardial Infarction with Support Vector Machine Classifier Using Grasshopper Optimization Algorithm

Safdarian, Naser; Nezhad, Shadi Yoosefian Dezfuli; Dabanloo, Nader Jafarnia

doi:10.4103/jmss.jmss_24_20

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Tree-based ML algorithmic models report high accuracy like 97.88% for Random Forest (RF), 98.23% for Extreme Gradient Boosting (XGBoost), and 98.03% for Light Gradient Boosting Machine (LightGBM) and outperform other ML algorithms, and their performance is comparable to the highend DL architectures [2]. Apart from tree-based algorithms, other ML algorithms like Support Vector Machine (SVM) give better accuracy like 96.67% with morphological & temporal features [17], 98.33% with 220 features [18] & 100% [19], along with grasshopper optimization algorithm, 99.9% for cubic models & 99.8% for a quadratic model [20], 95.1% for Adaptive boosting (AdaBoost) [17] algorithm, and 94.9% for Logistic Regression [21].…”

Section: Literature Surveymentioning

confidence: 99%

Convolution-Based Heterogeneous Activation Facility for Effective Machine Learning of ECG Signals

S.,

Narayanan

2023

CMC

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Machine Learning (ML) and Deep Learning (DL) technologies are revolutionizing the medical domain, especially with Electrocardiogram (ECG), by providing new tools and techniques for diagnosing, treating, and preventing diseases. However, DL architectures are computationally more demanding. In recent years, researchers have focused on combining the computationally less intensive portion of the DL architectures with ML approaches, say for example, combining the convolutional layer blocks of Convolution Neural Networks (CNNs) into ML algorithms such as Extreme Gradient Boosting (XGBoost) and K-Nearest Neighbor (KNN) resulting in CNN-XGBoost and CNN-KNN, respectively. However, these approaches are homogenous in the sense that they use a fixed Activation Function (AFs) in the sequence of convolution and pooling layers, thereby limiting the ability to capture unique features. Since various AFs are readily available and each could capture unique features, we propose a Convolutionbased Heterogeneous Activation Facility (CHAF) which uses multiple AFs in the convolution layer blocks, one for each block, with a motivation of extracting features in a better manner to improve the accuracy. The proposed CHAF approach is validated on PTB and shown to outperform the homogeneous approaches such as CNN-KNN and CNN-XGBoost. For PTB dataset, proposed CHAF-KNN has an accuracy of 99.55% and an F1 score of 99.68% in just 0.008 s, outperforming the state-of-the-art CNN-XGBoost which has an accuracy of 99.38% and an F1 score of 99.32% in 1.23 s. To validate the generality of the proposed CHAF, experiments were repeated on MIT-BIH dataset, and the proposed CHAF-KNN is shown to outperform CNN-KNN and CNN-XGBoost.

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