Interpretable Heart Disease Detection Model for IoT-Enabled WBAN Systems

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Damilola D. Olatinwo;Adnan M. Abu-Mahfouz;Gerhard P. Hancke;Hermanus C. Myburgh
{"title":"Interpretable Heart Disease Detection Model for IoT-Enabled WBAN Systems","authors":"Damilola D. Olatinwo;Adnan M. Abu-Mahfouz;Gerhard P. Hancke;Hermanus C. Myburgh","doi":"10.1109/JSEN.2024.3520866","DOIUrl":null,"url":null,"abstract":"Heart disease is a leading global health concern, contributing to significant mortality rates. It encompasses a range of conditions affecting the heart, leading to complications such as blocked blood vessels, myocardial infarction, chest pain, and stroke. This study presents an interpretable heart disease detection model specifically designed for Internet of Things (IoT)-enabled wireless body area networks (WBANs). Our approach employs a highway bidirectional gated recurrent unit (BiGRU) network to accurately detect heart disease patients. To enhance the model performance, we address critical data preprocessing challenges, such as outliers in data, class imbalance, and feature selection. We employ a robust scaler data transformation method to mitigate the impact of outliers. The synthetic minority oversampling technique (SMOTE) is applied to address the imbalance in the dataset. We utilize the SelectKBest algorithm with the ANOVA F-test scoring function to select the most relevant features to improve the model efficiency. The dataset is partitioned into training, validation, and testing sets to ensure model generalization. Hyperparameter optimization is performed using a random search method to determine the optimal model architecture. Furthermore, a highway network mechanism is incorporated to enhance information flow, leading to improved training efficiency and detection accuracy. To ensure clinical relevance and acceptability, we employ the SHapley Additive exPlanations (SHAP) technique to provide insights into the model’s decision-making process. Evaluation of unseen test data demonstrates that our proposed model outperforms existing approaches by 1%–9% in terms of detection accuracy.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 3","pages":"5457-5469"},"PeriodicalIF":4.3000,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/10817506/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

Heart disease is a leading global health concern, contributing to significant mortality rates. It encompasses a range of conditions affecting the heart, leading to complications such as blocked blood vessels, myocardial infarction, chest pain, and stroke. This study presents an interpretable heart disease detection model specifically designed for Internet of Things (IoT)-enabled wireless body area networks (WBANs). Our approach employs a highway bidirectional gated recurrent unit (BiGRU) network to accurately detect heart disease patients. To enhance the model performance, we address critical data preprocessing challenges, such as outliers in data, class imbalance, and feature selection. We employ a robust scaler data transformation method to mitigate the impact of outliers. The synthetic minority oversampling technique (SMOTE) is applied to address the imbalance in the dataset. We utilize the SelectKBest algorithm with the ANOVA F-test scoring function to select the most relevant features to improve the model efficiency. The dataset is partitioned into training, validation, and testing sets to ensure model generalization. Hyperparameter optimization is performed using a random search method to determine the optimal model architecture. Furthermore, a highway network mechanism is incorporated to enhance information flow, leading to improved training efficiency and detection accuracy. To ensure clinical relevance and acceptability, we employ the SHapley Additive exPlanations (SHAP) technique to provide insights into the model’s decision-making process. Evaluation of unseen test data demonstrates that our proposed model outperforms existing approaches by 1%–9% in terms of detection accuracy.
求助全文
约1分钟内获得全文 求助全文
来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信