Internet of Medical Things (IoMT) Enabled Intelligent System for Chronic Disease Prediction Using Deep Machine Learning in Healthcare 5.0

Abstract

Accurately diagnosing human diseases is still challenging, even with the advances in Healthcare 5.0, especially with chronic diseases. The Internet of Medical Things (IoMT) has grown quickly worldwide, from tiny wearables to extensive applications in many different industries. Osteoarthritis (OA) is a prevalent chronic disease that has a major negative influence on life quality, especially in older people. Osteoarthritis is a common chronic joint disease that contributes significantly to morbidity and disability globally. It is the most frequent kind of OA. Knee Osteoarthritis (KOA) has a significant negative influence on the quality of life for those affected and presents an increasing challenge to public health systems as the world's population ages and life expectancy rises. Even though the precise causes of osteoarthritis (OA) are yet unknown, the complicated condition frequently affects joints that experience heavy weight and repetitive action. The knee joint is particularly susceptible because of its intricate structure and function as a weight-bearing joint our work investigates the use of deep machine learning techniques, including transfer learning, with X-ray image datasets to predict KOA in an Internet of Medical Things (IoMT) enabled system. This strategy is in line with the ideas of Healthcare 5.0, which places a strong emphasis on using cutting-edge technology to provide individualized, patient-centered care. This method makes use of the ResNet 18 architecture and transfer learning to produce precise and effective predictions from knee X-ray images. Our system's integration with the Internet of Medical Things (IoMT) facilitates real-time data processing and gathering, improving the predictive model's usability and accessibility in clinical settings. Using a dataset of knee X-ray images, the suggested model was trained and validated, yielding a high training accuracy of 98.26% and a validation accuracy of 95.79%. These findings support the model's efficacy in correctly diagnosing osteoarthritis, facilitating prompt diagnosis and treatment. Our results highlight the potential of IoMT and deep machine learning to improve personalized healthcare, especially for the treatment of long-term conditions like osteoarthritis. By incorporating these technologies into Healthcare 5.0 frameworks, it may be possible to improve outcomes and lessen the burden of chronic disease while providing more focused and patient-centered care. We seek to improve OA early diagnosis and customized therapy by utilizing IoMT and state-of-the-art deep machine learning algorithms, utilizing x-ray image data for improved healthcare results.