Machine learning in molecular communication and applications for health monitoring networks

The world has been greatly affected by increased utilization of mobile methods as well as smart devices in field of health. Health professionals are increasingly utilizing these technologies' advantages, resulting in a significant improvement in clinical health care. For this purpose, machine learning (ML) as well as Internet of Things can be utilized effectively. This study aims to propose a novel data analysis method for a health monitoring system based on ML. Goal of research is to create a ML-based smart health monitoring method. It helps the doctors keep an eye on patients from a distance as well as take periodic actions if they need to. Utilizing wearable sensors, a set of five parameters—the electrocardiogram, pulse rate, pressure, temperature, and position detection—have been identified. Kernelized component vector neural networks are used to choose the features in the input data. Then, a sparse attention-based convolutional neural network with a structural search algorithm was used to classify the selected features. For a variety of datasets, the proposed technique attained validation accuracy 95%, training accuracy 92%, RMSE 52%, F-measure 53%, and sensitivity 77%.