Earticle

초록

영어

With the continuous acceleration of economic and social development, people gradually pay attention to their health, improve their living environment, diet, strengthen exercise, and even conduct regular health examination, to ensure that they always understand the health status. Even so, people still face many health problems, and the number of chronic diseases is increasing. Recently, COVID-19 has also reminded people that public health problems are also facing severe challenges. With the development of artificial intelligence equipment and technology, medical diagnosis expert systems based on big data have become a topic of concern to many researchers. At present, there are many algorithms that can help computers initially diagnose diseases for patients, but they want to improve the accuracy of diagnosis. And taking into account the pathology that varies from person to person, the health diagnosis expert system urgently needs a new algorithm to improve accuracy. Through the understanding of classic algorithms, this paper has optimized it, and finally proved through experiments that the combined classification algorithm improved by latent factors can meet the needs of medical intelligent diagnosis.

목차

Abstract
1. Related theories and technologies of smart medical service system
1.1 Expert system
1.2 Knowledge Base and inference engine
1.3 Data mining technology
2. The role of different algorithms in the medical diagnosis system
2.1 Association rule mining and knowledge discovery
2.2 Classical classification algorithm
2.3 Mining potential factors for medical diagnosis
2.4 Combined classification algorithm based on latent factor
3. Algorithm improvement
3.1 Improved classification based on combination
3.2 Improved combination classification algorithm based on latent factor
3.3 The flow of the improved algorithm is described in detail
4. Comparison and analysis of results before and after improvement
5. Conclusion
Acknowledgememt
References

키워드

저자

• Yu Linjun [ 1Assistant Professor, School of Electronic Commerce, Jiujiang University, Jiangxi, China ]
• Yun-Jeong Kang [ Assistant Professor, College of Convergence Liberal Arts, Wonkwang University, Republic of Korea ]
• Dong-Oun Choi [ Professor, Department of Computer Software Engineering, Wonkwang University, Republic of Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380