An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks
Maternal mortality (MM) is considered one of the major worldwide concerns. Despite the advances of artificial intelligence (AI) in healthcare, the lack of transparency in AI models leads to reluctance to adopt them. Employing explainable artificial intelligence (XAI) thus helps improve the transpare...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/16/11/411 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846153411499655168 |
|---|---|
| author | Sherine Nagy Saleh Mazen Nabil Elagamy Yasmine N. M. Saleh Radwa Ahmed Osman |
| author_facet | Sherine Nagy Saleh Mazen Nabil Elagamy Yasmine N. M. Saleh Radwa Ahmed Osman |
| author_sort | Sherine Nagy Saleh |
| collection | DOAJ |
| description | Maternal mortality (MM) is considered one of the major worldwide concerns. Despite the advances of artificial intelligence (AI) in healthcare, the lack of transparency in AI models leads to reluctance to adopt them. Employing explainable artificial intelligence (XAI) thus helps improve the transparency and effectiveness of AI-driven healthcare solutions. Accordingly, this article proposes a complete framework integrating an Internet of Medical Things (IoMT) architecture with an XAI-based deep learning model. The IoMT system continuously monitors pregnant women’s vital signs, while the XAI model analyzes the collected data to identify risk factors and generate actionable insights. Additionally, an efficient IoMT transmission model is developed to ensure reliable data transfer with the best-required system quality of service (QoS). Further analytics are performed on the data collected from different regions in a country to address high-risk cities. The experiments demonstrate the effectiveness of the proposed framework by achieving an accuracy of 80% for patients and 92.6% for regional risk prediction and providing interpretable explanations. The XAI-generated insights empower healthcare providers to make informed decisions and implement timely interventions. Furthermore, the IoMT transmission model ensures efficient and secure data transfer. |
| format | Article |
| id | doaj-art-0781b1595d1543a39d30c6fc0dcb1fc5 |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-0781b1595d1543a39d30c6fc0dcb1fc52024-11-26T18:05:14ZengMDPI AGFuture Internet1999-59032024-11-01161141110.3390/fi16110411An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality RisksSherine Nagy Saleh0Mazen Nabil Elagamy1Yasmine N. M. Saleh2Radwa Ahmed Osman3Computer Engineering Department, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Alexandria 1029, EgyptComputer Engineering Department, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Alexandria 1029, EgyptComputer Science Department, College of Computing and Information Technology, Arab Academy for Science and Technology (AAST), Alexandria 1029, EgyptBasic and Applied Science Department, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Alexandria 1029, EgyptMaternal mortality (MM) is considered one of the major worldwide concerns. Despite the advances of artificial intelligence (AI) in healthcare, the lack of transparency in AI models leads to reluctance to adopt them. Employing explainable artificial intelligence (XAI) thus helps improve the transparency and effectiveness of AI-driven healthcare solutions. Accordingly, this article proposes a complete framework integrating an Internet of Medical Things (IoMT) architecture with an XAI-based deep learning model. The IoMT system continuously monitors pregnant women’s vital signs, while the XAI model analyzes the collected data to identify risk factors and generate actionable insights. Additionally, an efficient IoMT transmission model is developed to ensure reliable data transfer with the best-required system quality of service (QoS). Further analytics are performed on the data collected from different regions in a country to address high-risk cities. The experiments demonstrate the effectiveness of the proposed framework by achieving an accuracy of 80% for patients and 92.6% for regional risk prediction and providing interpretable explanations. The XAI-generated insights empower healthcare providers to make informed decisions and implement timely interventions. Furthermore, the IoMT transmission model ensures efficient and secure data transfer.https://www.mdpi.com/1999-5903/16/11/411explainable artificial intelligenceIoMTmaternal mortalitysecurity and privacyenergy efficiencymachine learning |
| spellingShingle | Sherine Nagy Saleh Mazen Nabil Elagamy Yasmine N. M. Saleh Radwa Ahmed Osman An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks Future Internet explainable artificial intelligence IoMT maternal mortality security and privacy energy efficiency machine learning |
| title | An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks |
| title_full | An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks |
| title_fullStr | An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks |
| title_full_unstemmed | An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks |
| title_short | An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks |
| title_sort | explainable deep learning enhanced iomt model for effective monitoring and reduction of maternal mortality risks |
| topic | explainable artificial intelligence IoMT maternal mortality security and privacy energy efficiency machine learning |
| url | https://www.mdpi.com/1999-5903/16/11/411 |
| work_keys_str_mv | AT sherinenagysaleh anexplainabledeeplearningenhancediomtmodelforeffectivemonitoringandreductionofmaternalmortalityrisks AT mazennabilelagamy anexplainabledeeplearningenhancediomtmodelforeffectivemonitoringandreductionofmaternalmortalityrisks AT yasminenmsaleh anexplainabledeeplearningenhancediomtmodelforeffectivemonitoringandreductionofmaternalmortalityrisks AT radwaahmedosman anexplainabledeeplearningenhancediomtmodelforeffectivemonitoringandreductionofmaternalmortalityrisks AT sherinenagysaleh explainabledeeplearningenhancediomtmodelforeffectivemonitoringandreductionofmaternalmortalityrisks AT mazennabilelagamy explainabledeeplearningenhancediomtmodelforeffectivemonitoringandreductionofmaternalmortalityrisks AT yasminenmsaleh explainabledeeplearningenhancediomtmodelforeffectivemonitoringandreductionofmaternalmortalityrisks AT radwaahmedosman explainabledeeplearningenhancediomtmodelforeffectivemonitoringandreductionofmaternalmortalityrisks |