Multi-layer Perceptron for Heart Failure Detection Using SMOTE Technique

Bokhare, Anuja; Bhagat, Aarti; Bhalodia, Raj

doi:10.1007/s42979-022-01596-x

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…In SMOTE, additional minority samples are created along the line segment among the minority samples, although with no indication of any kind to the samples available in the confrontational majority class. SMOTE has been applied in various domains, including finance (Sun et al, 2020), fraud detection (Xia et al, 2023), medical diagnosis (Bokhare et al, 2023, Kamarulzalis et al, 2018 and image classification (Khan & Sheikh, 2023). It has shown promising results in improving the classification accuracy of models in these domains.…”

Section: Synthetic Samplingmentioning

confidence: 99%

Cluster-Based Oversampling with Area Extraction from Representative Points for Class Imbalance Learning

Farou

Wang

Horváth

2023

Preprint

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Section: Synthetic Samplingmentioning

confidence: 99%

Cluster-Based Oversampling with Area Extraction from Representative Points for Class Imbalance Learning

Farou

Wang

Horváth

2023

Preprint

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A Novel Approach for Specification Testing on Heart Disease Detection Using Feed-Forward Neural Network

Naresh,

Darshan,

Srinidhi

et al. 2023

SN COMPUT. SCI.

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The proposed work highlights the importance of testing in machine-learning applications and the ensuing need to increase model quality to decrease the likelihood of errors. The proposed work considers patient health information that may be utilized for decision-making or prediction utilizing various computations, and in this instance, it emphasizes the development of artificial neural networks with the multilayer perceptron method to forecast cardiac abnormalities. The dataset employed in this work includes information about the patient’s demographics, clinical measures, and medical background. Utilizing this labelled dataset, one may estimate if each patient has heart disease or not. We developed a GUI to collect data about a new patient to accomplish the task specified. After the model is generated, effective functionality testing was performed on the model. As a result of the testing report’s assistance in identifying faults and defects, the artificial neural network’s accuracy ultimately improved as a result of its improved accuracy following correction. After undergoing feature testing, the application’s accuracy ascended to 96.69%, which is a higher percentage than was attained using any alternative method.

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Cluster-based oversampling with area extraction from representative points for class imbalance learning

Farou,

Wang,

Horváth

2024

Intelligent Systems with Applications

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Multi-layer Perceptron for Heart Failure Detection Using SMOTE Technique

Cited by 6 publications

References 16 publications

Cluster-Based Oversampling with Area Extraction from Representative Points for Class Imbalance Learning

Cluster-Based Oversampling with Area Extraction from Representative Points for Class Imbalance Learning

A Novel Approach for Specification Testing on Heart Disease Detection Using Feed-Forward Neural Network

Cluster-based oversampling with area extraction from representative points for class imbalance learning

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