An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks

Sherine Nagy Saleh (), Mazen Nabil Elagamy, Yasmine N. M. Saleh and Radwa Ahmed Osman
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Sherine Nagy Saleh: Computer Engineering Department, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Alexandria 1029, Egypt
Mazen Nabil Elagamy: Computer Engineering Department, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Alexandria 1029, Egypt
Yasmine N. M. Saleh: Computer Science Department, College of Computing and Information Technology, Arab Academy for Science and Technology (AAST), Alexandria 1029, Egypt
Radwa Ahmed Osman: Basic and Applied Science Department, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Alexandria 1029, Egypt

Future Internet, 2024, vol. 16, issue 11, 1-23

Abstract: 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.

Keywords: explainable artificial intelligence; IoMT; maternal mortality; security and privacy; energy efficiency; machine learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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