A cancer diagnosis transformer model based on medical IoT data for clinical measurements in predictive care systems

Abstract

Introduction: In recent years, advancements in information and communication technology (ICT) and the internet of things (IoT) have revolutionized the healthcare industry, enabling the collection, analysis, and utilization of medical data to improved patient care. One critical area of focus is the development of predictive care systems for early diagnosis and treatment of cancer and disease.
Methods: Leveraging medical IoT data, this study proposes a novel approach based on transformer model for disease diagnosis. In this paper, features are first extracted from IoT images using a transformer network. The network utilizes a convolutional neural network (CNN) in the encoder part to extract suitable features and employs decoder layers along with attention mechanisms in the decoder part. In the next step, considering that the extracted features have high dimensions and many of these features are irrelevant and redundant, relevant features are selected using the Harris hawk optimization algorithm.
Results: Various classifiers are used to label the input data. The proposed method is evaluated using a dataset consisting of 5 classes for testing and evaluation, and all results are provided into tables and plots.
Conclusion: The experimental results demonstrate that the proposed method acceptable performance compared to other methods.

Keywords: Cancer diagnosis, Transformer model, Medical IoT data, Clinical measurement, Predictive care systems, Deep learning