ISSN :2582-9793

The Feasibility of Applying Artificial Intelligence Detection Technology in Predicting the Risk of Hypertension

Original Research (Published On: 05-Sep-2023 )
The Feasibility of Applying Artificial Intelligence Detection Technology in Predicting the Risk of Hypertension
DOI : 10.54364/AAIML.2023.1179

Chung Te Ting and Wen-Fu Yang

Adv. Artif. Intell. Mach. Learn., 3 (3):1340-1351

Chung Te Ting : Department of Tourism, Food & Beverage Management, Chang Jung Christian University, Tainan 71101, Taiwan

Wen-Fu Yang : The Ph.D. Program in Business and Operations Management

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DOI: 10.54364/AAIML.2023.1179

Article History: Received on: 15-Jun-23, Accepted on: 28-Aug-23, Published on: 05-Sep-23

Corresponding Author: Chung Te Ting

Email: ctting@mail.cjcu.edu.tw

Citation: Wen-Fu Yang , Hsiu-Hao Liu, Chung Te Ting (2023). The Feasibility of Applying Artificial Intelligence Detection Technology in Predicting the Risk of Hypertension. Adv. Artif. Intell. Mach. Learn., 3 (3 ):1340-1351


Abstract

    

According to statistics from the World Health Organization (WHO) in 2022, cardiovascular diseases account for the largest proportion among noncommunicable diseases, causing approximately 17.9 million deaths annually. The next leading causes are cancer (9.3 million people), chronic respiratory diseases (4.1 million people), and diabetes (2 million people, including deaths from kidney diseases caused by diabetes). These four categories of diseases contribute to over 80% of premature deaths from noncommunicable diseases. Therefore, preventing the occurrence of diseases and understanding the functional status of various organ systems in the body has become crucially important. This study utilized techniques developed in the field of preventive medicine in Europe and the United States, combined with artificial intelligence detection technology, to analyze and compare the big data obtained from extracted organ cell functional response data. This analysis helps infer the functional status, developmental trends, and probabilities of diseases that may occur in the organs. These results can serve as a basis for individuals to make adjustments and reduce health risks. The study adopted a case study approach, collecting artificial intelligence detection data from 12 cases, while conducting cross-analysis with biochemical test data and body mass index to confirm the feasibility of artificial intelligence detection in predicting the risk of hypertension. Through the analysis and comparison, we found a high degree of correlation among the three test results. Therefore, the results of this study can be a reference for relevant professionals in academia, government, and industry.

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