IoT-Driven Healthcare Monitoring with Explainable Machine Learning Models

Abstract

The need for this work arises from the critical importance of IoT-driven healthcare monitoring. In an era marked by technological advancements, the healthcare sector has not been left untouched. The ability to monitor patients' health remotely through IoT devices has emerged as a promising solution, offering real-time data for healthcare providers. However, amidst this promise, there remain significant challenges. Existing approaches in this domain have limitations. They often lack transparency and interpretability, making it challenging to trust the decisions made by machine learning models. Moreover, their performance metrics often fall short of achieving optimal precision, accuracy, recall, AUC, and speed, which are crucial in healthcare applications where timely and accurate decisions. In response to these challenges, this paper presents a novel approach. The proposed model leverages Convolutional Neural Networks (CNN) and integrates Deep Shap and GridCAM++ techniques to offer a more explainable and interpretable solution for IoT-driven healthcare monitoring. This fusion of methods enhances the model's transparency, allowing healthcare professionals to understand the rationale behind its decisions. The advantages of this approach are multifold. First and foremost, it enhances the precision, accuracy, recall, and AUC by 5.5%, 5.9%, 6.5%, and 8.3%, respectively, when compared to existing methods. These improvements translate to more reliable diagnoses and decisions in healthcare. Additionally, the model achieves a 4.9% boost in speed, ensuring that critical decisions can be made swiftly, reducing the time between data collection and actions. The impact of this work is substantial, it paves the way for more trustworthy and efficient IoT-driven healthcare monitoring systems, addressing the limitations of existing approaches. With improved performance metrics and enhanced explainability, healthcare professionals can make more informed decisions, leading to better patient outcomes. Ultimately, this paper contributes to the advancement of healthcare technology, bringing us closer to a future where IoT-enabled monitoring plays a pivotal role in improving patient care sets.