Transfer learning-based hybrid VGG16-machine learning approach for heart disease detection with explainable artificial intelligence.

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Artificial Intelligence Pub Date : 2025-02-25 eCollection Date: 2025-01-01 DOI:10.3389/frai.2025.1504281
Eshetie Gizachew Addisu, Tahayu Gizachew Yirga, Hailu Gizachew Yirga, Alemu Demeke Yehuala
{"title":"Transfer learning-based hybrid VGG16-machine learning approach for heart disease detection with explainable artificial intelligence.","authors":"Eshetie Gizachew Addisu, Tahayu Gizachew Yirga, Hailu Gizachew Yirga, Alemu Demeke Yehuala","doi":"10.3389/frai.2025.1504281","DOIUrl":null,"url":null,"abstract":"<p><p>Heart disease is a leading cause of mortality worldwide, making accurate early detection essential for effective treatment and management. This study introduces a novel hybrid machine-learning approach that combines transfer learning using the VGG16 convolutional neural network (CNN) with various machine-learning classifiers for heart disease detection. A conditional tabular generative adversarial network (CTGAN) was employed to generate synthetic data samples from actual datasets; these were evaluated using statistical metrics, correlation analysis, and domain expert assessments to ensure the quality of the synthetic datasets. The dataset comprises tabular data with 13 features, which are reshaped into an image-like format and resized to 224x224x3 to meet the input requirements of the VGG16 model. Feature extraction is performed using VGG16, and the extracted features are then fused with the original tabular data. This combined feature set is then used to train various machine learning models, including Support Vector Machines (SVM), Gradient Boosting, Random Forest, Logistic Regression, K-nearest neighbors (KNN), and Decision Trees. Among these models, the VGG16-Random Forest hybrid achieved notable results across all evaluation metrics, including 92% accuracy, 91.3% precision, 92.2% recall, 91.82% specificity, 92.2% sensitivity, and 91.75% F1-score. The hybrid models were also evaluated using unseen datasets to assess the generalizability of the proposed approaches, with the VGG16-Random Forest combination showing relatively promising results. Additionally, explainability is integrated into the model using SHAP values, providing insights into the contribution of each feature to the model's predictions. This hybrid VGG16-ML approach demonstrates the potential for highly accurate and interpretable heart disease detection, offering valuable support in clinical decision-making processes.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1504281"},"PeriodicalIF":3.0000,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893864/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frai.2025.1504281","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Heart disease is a leading cause of mortality worldwide, making accurate early detection essential for effective treatment and management. This study introduces a novel hybrid machine-learning approach that combines transfer learning using the VGG16 convolutional neural network (CNN) with various machine-learning classifiers for heart disease detection. A conditional tabular generative adversarial network (CTGAN) was employed to generate synthetic data samples from actual datasets; these were evaluated using statistical metrics, correlation analysis, and domain expert assessments to ensure the quality of the synthetic datasets. The dataset comprises tabular data with 13 features, which are reshaped into an image-like format and resized to 224x224x3 to meet the input requirements of the VGG16 model. Feature extraction is performed using VGG16, and the extracted features are then fused with the original tabular data. This combined feature set is then used to train various machine learning models, including Support Vector Machines (SVM), Gradient Boosting, Random Forest, Logistic Regression, K-nearest neighbors (KNN), and Decision Trees. Among these models, the VGG16-Random Forest hybrid achieved notable results across all evaluation metrics, including 92% accuracy, 91.3% precision, 92.2% recall, 91.82% specificity, 92.2% sensitivity, and 91.75% F1-score. The hybrid models were also evaluated using unseen datasets to assess the generalizability of the proposed approaches, with the VGG16-Random Forest combination showing relatively promising results. Additionally, explainability is integrated into the model using SHAP values, providing insights into the contribution of each feature to the model's predictions. This hybrid VGG16-ML approach demonstrates the potential for highly accurate and interpretable heart disease detection, offering valuable support in clinical decision-making processes.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
期刊最新文献
Comparison of 3D and 2D area measurement of acute burn wounds with LiDAR technique and deep learning model. Advancements in cache management: a review of machine learning innovations for enhanced performance and security. Transfer learning-based hybrid VGG16-machine learning approach for heart disease detection with explainable artificial intelligence. AI, universal basic income, and power: symbolic violence in the tech elite's narrative. General SIR model for visible and hidden epidemic dynamics.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1