Advanced Machine Learning Algorithms for 1Predictive Analytics in Healthcare to Enhance Patient Outcomes with Data-Driven Insights

This research aims to explore the use of modern complex defensive machine learning algorithms in the provision of predictive analytics for health improvement. Incorporating electronic health records, medical image information, and genomic data the work proposes and assesses multiple machine learning models including deep learning, ensemble learning, and reinforcement learning. The main findings shown very high performance in disease prediction, in chronic illness and in the application of efficiency improvements in hospitals as a whole. Overall, such threats as data quality, data privacy, and model interpretability have been discussed in the research and emphasize the importance of the machine learning approach in healthcare. It is highlighted that further development and integration of the interdisciplinary approaches as well as the use of the transparent and interpretable artificial intelligence and machine learning models can be urgent to increase the acceptance among the healthcare staff. This paper enhances the literature on applying machine learning in healthcare systems adding to the repertoire of knowledge for improved patient care.

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Publication Details

Published
2024-11-21
DOI
https://doi.org/10.1109/icraset63057.2024.10895597
Citations
7
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
4.95
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article

Advanced Machine Learning Algorithms for 1Predictive Analytics in Healthcare to Enhance Patient Outcomes with Data-Driven Insights

P. S. Ramesh, A. Ponmalar, J. Jagannathan, Rajkumar Pandiarajan et al.
7 citations
Artificial Intelligence in Healthcare
4.95
article

Advanced Machine Learning Algorithms for 1Predictive Analytics in Healthcare to Enhance Patient Outcomes with Data-Driven Insights

P. S. Ramesh, A. Ponmalar, J. Jagannathan, Rajkumar Pandiarajan, S. Sudha
article en
7 citations

Abstract

This research aims to explore the use of modern complex defensive machine learning algorithms in the provision of predictive analytics for health improvement. Incorporating electronic health records, medical image information, and genomic data the work proposes and assesses multiple machine learning models including deep learning, ensemble learning, and reinforcement learning. The main findings shown very high performance in disease prediction, in chronic illness and in the application of efficiency improvements in hospitals as a whole. Overall, such threats as data quality, data privacy, and model interpretability have been discussed in the research and emphasize the importance of the machine learning approach in healthcare. It is highlighted that further development and integration of the interdisciplinary approaches as well as the use of the transparent and interpretable artificial intelligence and machine learning models can be urgent to increase the acceptance among the healthcare staff. This paper enhances the literature on applying machine learning in healthcare systems adding to the repertoire of knowledge for improved patient care.

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN), Dr. M.G.R. Educational and Research Institute (IN), R.M.D. Engineering College, Saveetha University (IN), Vellore Institute of Technology University (IN), Kalasalingam Academy of Research and Education (IN)
Openalex Percentile: Top 4%
Artificial Intelligence in Healthcare
4.95
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