pp. 135-140
S&M3896 Technical paper of Special Issue https://doi.org/10.18494/SAM5326 Published: January 22, 2025 Sensing Effective Healthcare through Artificial Intelligence: Analysis of the Hypertension Topic [PDF] Wan-I Lee and Tzu-Huang Chang (Received August 22, 2024; Accepted January 6, 2025) Keywords: machine learning, edge computing, Internet of Things, dynamic demolding comparison, APP
Owing to Taiwan’s aging population, many patients suffer from chronic diseases. The number of people seeking medical treatment for chronic diseases in 2023 was roughly 12.86 million. Approximately one in two people suffered from a chronic disease. The incidence of chronic comorbidities is more than 7 million people living with more chronic conditions.(16) Prevention can be achieved through healthcare. In order to find the beyond-compare healthcare model, a healthcare system that can collect and analyze long-term data through three core variables (daily exercise, diet, and body fat) should be developed. We applied linear regression as the basis of machine learning, a machine learning modeling approach built into the front-end sensing and conversion units (with edge computing). Dynamic disassembly and comparison results are transmitted to personal mobile phones through the Internet of Things and Line APP.(16) This allows users to understand the best individual healthcare model and simplify tedious procedures to achieve precise healthcare goals.
Corresponding author: Wan-I LeeThis work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Wan-I Lee and Tzu-Huang Chang, Sensing Effective Healthcare through Artificial Intelligence: Analysis of the Hypertension Topic, Sens. Mater., Vol. 37, No. 1, 2025, p. 135-140. |