# Internet of Medical Things Enabled Multimodal Framework: Deep Machine Learning for Chronic Cardiac Disease Prediction in Healthcare 5.0

## TL;DR

This paper introduces a deep learning framework using ECG images and IoMT to predict chronic heart diseases with high accuracy, supporting early detection and personalized treatment.

## Contribution

A multimodal CNN model is proposed for classifying multiple cardiac conditions using ECG images, achieving high accuracy in a Healthcare 5.0 context.

## Key findings

- The CNN model achieved 97.18% training accuracy and 94.34% validation accuracy in classifying various heart conditions from ECG images.
- Integration of ECG data from diverse sources improves the identification of cardiac irregularities and diagnostic accuracy.
- The framework supports a human-centered Healthcare 5.0 approach by combining IoMT, edge computing, and computational intelligence for timely and precise cardiac disease management.

## Abstract

Accurate and early detection of chronic heart disease is vital, as it remains one of the leading global causes of mortality. Despite advancements in Smart Healthcare 5.0 and modern information technologies, reliable diagnosis of cardiovascular conditions remains a significant challenge. The Internet of Medical Things (IoMT) enables seamless data exchange between medical devices, supporting more precise and timely management of cardiac diseases. This study employs convolutional neural networks (CNNs) on electrocardiogram (ECG) image datasets to classify multiple heart conditions. The datasets include ECG scans labelled as Abnormal Heartbeat (ANHB), Myocardial Infarction (MI), History of Myocardial Infarction (HOMI), Atrioventricular Heart Block (AHB), COVID‐19, Hypertrophic Cardiomyopathy (HMI), and Normal. A multimodal model integrating images of varying resolutions from two independent datasets was developed to improve classification performance. The proposed CNN model, trained and validated on preprocessed ECG images, achieved 97.18% training accuracy and 94.34% validation accuracy. By combining ECG data from diverse sources, the model enhances the identification of cardiac irregularities and provides a comprehensive diagnostic approach. This method demonstrates potential to support early detection, improve individualised treatment planning, and ultimately strengthen patient outcomes in managing chronic heart disease.

Healthcare 5.0 is grounded in a human centred approach, where digital systems work alongside clinicians to support physical health processes in a continuous and responsive manner. Within this context, cyber‐physical systems, or CPS, enable the close interaction between medical data, computational intelligence, and clinical decision‐making. Edge cloud computing supports this structure by allowing time‐critical processing to occur near the data source and computationally intensive analysis and model refinement. IoMT serves as the data collection layer by facilitating the acquisition of physiological signals from connected medical devices. Through the integration of these components, the proposed framework is positioned as a Healthcare 5.0‐orientated system.