Patient Health Monitoring using Feed Forward Neural Network with Cloud Based Internet of Things

The healthcare sector is under pressure to embrace new technologies that are available on the market in order to enhance the overall quality of their services. Telecommunications systems are combined with computers, interconnection, mobility, data storage, and information analytics. Technology that is centred on the Internet of Things (IoT) is the order of the day. Because of the limited availability of human resources and infrastructure, it is becoming more necessary to monitor chronic patients on a continual basis as their conditions worsen. A cloud-based architecture, which can handle all of the aforementioned concerns, may offer effective solutions to the health-care sector. In order to create software that combines cloud computing and mobile technologies for health care monitoring systems, we have set a goal of developing software. A technique developed by Higuchi is used to extract steady fractal values from electrocardiogram (ECG) data, which has never been tried before by any other researcher in the area of creating a computer-aided diagnostic system for arrhythmia. Based on the findings, it can be concluded that the support vector machine has achieved the highest possible classification accuracy for fractal features. While being compared to the other two classifiers, which are the feed forward and feedback neural network models, the support vector machine outperforms them both. In addition, it should be highlighted that the sensitivity of the feed forward neural network and the support vector machine provide results that are comparable (92.08 percent and 90.36 percent, respectively).