{"title":"Improving Cardiovascular Disease Prediction With Deep Learning and Correlation-Aware SMOTE","authors":"Maria Trigka;Elias Dritsas","doi":"10.1109/ACCESS.2025.3549417","DOIUrl":null,"url":null,"abstract":"Cardiovascular disease (CVD) ranks among the top causes of mortality globally, underscoring the urgent necessity for advanced predictive models to enhance early detection and preventative measures. In this direction, this study investigates the performance of five well-established deep learning (DL) models, namely Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Autoencoder in predicting CVD using a diverse patient dataset. To tackle the prevalent class imbalance issue in medical datasets, we introduce an enhanced Synthetic Minority Over-sampling Technique (SMOTE). This innovative technique enhances traditional SMOTE by incorporating feature correlations to produce more realistic synthetic samples. We compare model performance across three scenarios: without SMOTE, with traditional SMOTE, and with enhanced SMOTE, using metrics such as Accuracy, Precision, Recall, F1-Score, and Area Under the Curve (AUC). Our results show that the enhanced SMOTE significantly improves model performance, especially in recall and AUC-ROC. Notably, the CNN model with enhanced SMOTE prevailed, achieving the highest overall performance with an AUC of 0.90, an Accuracy of 0.91, a Precision of 0.89, a Recall of 0.86, and an F1-Score equal to 0.87, making it the most effective model in this study. This research highlights the potential of the enhanced SMOTE in developing robust predictive models for CVD, with broader implications for healthcare analytics.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"44590-44606"},"PeriodicalIF":3.4000,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916648","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10916648/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Cardiovascular disease (CVD) ranks among the top causes of mortality globally, underscoring the urgent necessity for advanced predictive models to enhance early detection and preventative measures. In this direction, this study investigates the performance of five well-established deep learning (DL) models, namely Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Autoencoder in predicting CVD using a diverse patient dataset. To tackle the prevalent class imbalance issue in medical datasets, we introduce an enhanced Synthetic Minority Over-sampling Technique (SMOTE). This innovative technique enhances traditional SMOTE by incorporating feature correlations to produce more realistic synthetic samples. We compare model performance across three scenarios: without SMOTE, with traditional SMOTE, and with enhanced SMOTE, using metrics such as Accuracy, Precision, Recall, F1-Score, and Area Under the Curve (AUC). Our results show that the enhanced SMOTE significantly improves model performance, especially in recall and AUC-ROC. Notably, the CNN model with enhanced SMOTE prevailed, achieving the highest overall performance with an AUC of 0.90, an Accuracy of 0.91, a Precision of 0.89, a Recall of 0.86, and an F1-Score equal to 0.87, making it the most effective model in this study. This research highlights the potential of the enhanced SMOTE in developing robust predictive models for CVD, with broader implications for healthcare analytics.
IEEE AccessCOMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍:
IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest.
IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on:
Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals.
Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering.
Development of new or improved fabrication or manufacturing techniques.
Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.