Machine Learning-based maternal health risk prediction model for IoMT framework

Abstract

The Internet of Things (IoT) is vital as it offers extensive applicability in various fields, including healthcare. In the context of the risk level during pregnancy, to monitor and predict abnormalities, IoT devices provide a means to collect real-time health data, enabling continuous monitoring and analysis in the Internet of Medical Things (IoMT) environments. By integrating IoT devices into the system, crucial signs such as Heart Rate (HR), Systolic and Diastolic Blood Pressure (BP), Fetal Movements (FM), and Temperature (T) can be tracked remotely and non-invasively. This allows for the timely detection of abnormalities or potential risk factors during pregnancy, empowering healthcare professionals to intervene proactively and provide personalized care. This research focuses on developing a system for observing and predicting the maternal risk level in the IoT environment, mainly in remote areas. The goal is to improve maternal health and reduce maternal and child mortality rates, a significant decline according to United Nations targets for 2030. The research utilizes analytical tools and Machine Learning (ML) algorithms to analyze health data and risk factors associated with pregnancy. The acquired dataset contains various risk factors categorized and classified based on intensity. After comparing different ML models’ experimental results, Exploratory Data Analysis (EDA) approaches to determine the most effective risk factors. The fine-tuned Random Forest Classifier (RF) achieves the highest accuracy of 93.14%. An Android-based application has also been developed to deploy the prediction model to determine risk levels based on the different parameters.

Providing Highest Privacy Preservation Scenario for Achieving Privacy in Confidential Data

“…Beyond legal methods to avoid information leaks, effective privacy protection in ML needs the unique properties of ML itself (Bettini and Riboni, 2015;Mondal et al, 2023). It necessitates building model structures and training procedures with privacy protection as a top priority, guaranteeing that sensitive personal information is inaccessible to unauthorized parties throughout the learning process.…”

A Secure Biometric-Based User Authentication Scheme for Cyber-Physical Systems in Healthcare

“…Therefore, experts are working to provide services remotely, particularly for elderly people (Pal et al, 2018). To overcome aforesaid challenges, a based IoT environment for remote healthcare monitoring (Mondal et al, 2023: Jain et al, 2023 using wireless sensor networks (WSNs) (Alghamdi et al, 2023) is one of the eminent solutions for helping older people independently manage good health and safely age in place. It is regarded as a novel paradigm within the realm of the Internet of Things (IoT), facilitated by the proliferation of Machineto-Machine communication, Wireless Sensor Networks, ubiquitous computing technology, Radio Frequency Identification (RFID), network communication infrastructure and evolving control methodologies (Rai et al, 2023;Dawn et al, 2023).…”

“…have the potential to leverage the proliferation of smart devices and wireless networks, enabling them to provide intelligent services which are driven by data from the physical environment. Further, IoT sensor device-based home care is becoming an integral part of the healthcare monitoring system (Mondal et al, 2023). Aiming to prevent elder and disabled people from being confined to institutions unnecessarily, this policy encourages people to age in place.…”