Machine Learning techniques to Predictive in Healthcare: Hepatitis C Diagnosis

Authors

• Hussein Alkattan, Department of System Programming, South Ural State University, Chelyabinsk, Russia.Follow
• Bashar Talib Al-Nuaimi, Computer Science Department, University of Diyala, Diyala 32001, Iraq.
• Alhumaima Ali Subhi, Electronic Computer Centre, University of Diyala, Diyala, Iraq.

Abstract

The accurate prediction and classification of medical data, such as Hepatitis C, play a significant role in upgrading symptomatic accuracy and treatment planning. This study utilized progressed machine learning algorithms, including RF, SVR, and Gradient Boosting, to analyze features and predict outcomes viably. By coordination robust preprocessing strategies and feature engineering, the models tended to missing values and categorical transformations, enabling exact predictions from complex datasets. The models accomplished high accuracy and unwavering quality in execution, as prove by comparative measurements and validation results. These discoveries highlight the potential of machine learning in increasing clinical decision-making and emphasize the require for advance research to optimize these methods for broader healthcare applications.

Recommended Citation

Alkattan, Hussein; Al-Nuaimi, Bashar Talib; and Subhi, Alhumaima Ali (2024) "Machine Learning techniques to Predictive in Healthcare: Hepatitis C Diagnosis," Mesopotamian Journal of Artificial Intelligence in Healthcare: Vol. 2: Iss. 1, Article 15.
DOI: https://doi.org/10.58496/MJAIH/2024/015

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.