Multiple feature selection based on an optimization strategy for causal analysis of health data DOI Creative Commons
Ruichen Cong, Ou Deng, Shoji Nishimura

et al.

Health Information Science and Systems, Journal Year: 2024, Volume and Issue: 12(1)

Published: Nov. 12, 2024

Recent advancements in information technology and wearable devices have revolutionized healthcare through health data analysis. Identifying significant relationships complex enhances public strategies. In analytics, causal graphs are important for investigating the among features. However, they face challenges owing to large number of features, complexity, computational demands. Feature selection methods useful addressing these challenges. this paper, we present a framework multiple feature based on an optimization strategy analysis data.

Language: Английский

Deep study on autonomous learning techniques for complex pattern recognition in interconnected information systems DOI
Zahra Mohtasham‐Amiri, Arash Heidari,

Nima Jafari

et al.

Computer Science Review, Journal Year: 2024, Volume and Issue: 54, P. 100666 - 100666

Published: Sept. 20, 2024

Language: Английский

Citations

15
Genetic factors, risk prediction and AI application of thrombotic diseases DOI Creative Commons
Rong Wang, Liang Tang, Yu Hu

et al.

Experimental Hematology and Oncology, Journal Year: 2024, Volume and Issue: 13(1)

Published: Aug. 27, 2024

Abstract In thrombotic diseases, coagulation, anticoagulation, and fibrinolysis are three key physiological processes that interact to maintain blood in an appropriate state within vessels. When these become imbalanced, such as excessive coagulation or reduced anticoagulant function, it can lead the formation of clots. Genetic factors play a significant role onset diseases exhibit regional ethnic variations. The decision whether initiate prophylactic therapy is matter clinicians must carefully consider, leading development various risk assessment scales clinical practice. Given considerable heterogeneity diagnosis treatment, researchers exploring application artificial intelligence medicine, including disease prediction, diagnosis, prevention, patient management. This paper reviews research progress on genetic involved analyzes advantages disadvantages commonly used characteristics ideal scoring scales, explores medical field, along with its future prospects.

Language: Английский

Citations

9
Neurodegenerative disorders: A Holistic study of the explainable artificial intelligence applications DOI Creative Commons
Hongyuan Wang, Shiva Toumaj, Arash Heidari

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 153, P. 110752 - 110752

Published: April 18, 2025

Language: Английский

Citations

0
Multiple feature selection based on an optimization strategy for causal analysis of health data DOI Creative Commons
Ruichen Cong, Ou Deng, Shoji Nishimura

et al.

Health Information Science and Systems, Journal Year: 2024, Volume and Issue: 12(1)

Published: Nov. 12, 2024

Recent advancements in information technology and wearable devices have revolutionized healthcare through health data analysis. Identifying significant relationships complex enhances public strategies. In analytics, causal graphs are important for investigating the among features. However, they face challenges owing to large number of features, complexity, computational demands. Feature selection methods useful addressing these challenges. this paper, we present a framework multiple feature based on an optimization strategy analysis data.

Language: Английский

Citations

0