Machine Learning in Disease Detection: Applications, Challenges, and Future Directions
Alt Text: Machine Learning in Disease Detection: Applications, Challenges, and Future Directions
Title: Machine Learning in Disease Detection: Applications, Challenges, and Future Directions
Caption: Exploring the role of machine learning in disease diagnosis, its challenges, and future implications.
Description: This paper discusses the applications of machine learning in disease detection, including cancer, cardiovascular diseases, infectious diseases, and neurodegenerative disorders, while examining challenges and future research directions.
Keywords: Machine Learning, Disease Detection, AI in Healthcare, Medical Diagnosis, Predictive Analytics
International Journal of Engineering and Techniques – Volume 10 Issue 3, May 2024
Eli Nora1, Karan Beyota1
1School of Computer Science, University de Santa, Spain.
Corresponding author: Karan Beyota.
Email: hesnob1991@gmail.com
Abstract
Machine learning (ML) has emerged as a transformative technology in the field of healthcare, particularly in disease detection. By leveraging vast amounts of data and sophisticated algorithms, ML can enhance the accuracy and efficiency of disease diagnosis, ultimately improving patient outcomes. This paper explores the applications of machine learning in disease detection, including cancer, cardiovascular diseases, infectious diseases, and neurodegenerative disorders. It also discusses the challenges faced in implementing ML in clinical practice and suggests potential future directions for research and application.
Keywords
Machine Learning, Disease Detection, AI in Healthcare, Medical Diagnosis, Predictive Analytics
How to Cite
Nora, E., Beyota, K., “Machine Learning in Disease Detection: Applications, Challenges, and Future Directions,” International Journal of Engineering and Techniques, Volume 10, Issue 3, May 2024. ISSN 2395-1303
Post Comment