Machine Learning Approaches for Predicting Dental Treatment Outcomes
Cover
PDF

Keywords

Machine learning
dental treatment outcomes
prediction
healthcare

How to Cite

[1]
Mei Ling, “Machine Learning Approaches for Predicting Dental Treatment Outcomes”, Journal of AI in Healthcare and Medicine, vol. 3, no. 1, pp. 9–19, Apr. 2023, Accessed: Nov. 10, 2024. [Online]. Available: https://healthsciencepub.com/index.php/jaihm/article/view/11

Abstract

Machine learning (ML) techniques have shown promise in various healthcare applications, including dentistry. This paper evaluates the use of ML approaches for predicting treatment outcomes in dental procedures. We review different ML algorithms and their applications in dental treatment outcome prediction, discussing the advantages, limitations, and challenges. The goal is to provide insights into how ML can enhance treatment planning and decision-making in dentistry, ultimately improving patient care and outcomes.

PDF

References

Reddy, Byrapu, and Surendranadha Reddy. "Evaluating The Data Analytics For Finance And Insurance Sectors For Industry 4.0." Tuijin Jishu/Journal of Propulsion Technology 44.4 (2023): 3871-3877.

Pillai, Aravind Sasidharan. "Advancements in Natural Language Processing for Automotive Virtual Assistants Enhancing User Experience and Safety." Journal of Computational Intelligence and Robotics 3.1 (2023): 27-36.

Vemuri, Navya, and Kamala Venigandla. "Autonomous DevOps: Integrating RPA, AI, and ML for Self-Optimizing Development Pipelines." Asian Journal of Multidisciplinary Research & Review 3.2 (2022): 214-231.

Reddy, Surendranadha Reddy Byrapu. "Predictive Analytics in Customer Relationship Management: Utilizing Big Data and AI to Drive Personalized Marketing Strategies." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 1-12.

Raparthi, Mohan, et al. "Advancements in Natural Language Processing-A Comprehensive Review of AI Techniques." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 1-10.

Downloads

Download data is not yet available.