Wearable Device Based on IoT in the Healthcare System for Disease Detection and Symptom Recognition

Humans in good shape face many challenges in their lives, such as food habits and climate change. The result must be aware of the health situation to survive. Lack of accurate patient information, preventive errors, data risks, overdiagnosis, and delayed implementation are challenges that health support services face. Wearable sensors that connect extensive data, data mining analysis for healthcare, and the Internet of things (IoT) have been proposed to solve this problem. This research, Disease Prediction and Symptom Recognition Model using IoT (DDSR-IoT) framework, is proposed for reasoning with regression rules to gather patient information. The Boltzmann network to train Artificial Intelligence (AI) feedback is introduced in the end. As a result, the broad interaction analysis of genomes is used to predict conditions. If those infections have affected people, emails are sent to warn them and provide them with prescriptions and medical advice. In the recommended approach, the experimental study resulted in an enhanced forecast rate of 97.4 percent and a precision of 97.42 percent.

Automated summary

The study introduces a Disease Prediction and Symptom Recognition Model using IoT technology, incorporating data mining analysis and AI feedback. Through comprehensive genome interaction analysis, conditions of patients can be successfully predicted and warning emails with medical advice are sent to them, resulting in an improved prediction rate and accuracy.