Version 1
: Received: 15 October 2023 / Approved: 16 October 2023 / Online: 16 October 2023 (09:33:57 CEST)
How to cite:
Santos, D. Predicting the Severity of Hepatitis C Using Machine Learning Models. Preprints2023, 2023100952. https://doi.org/10.20944/preprints202310.0952.v1
Santos, D. Predicting the Severity of Hepatitis C Using Machine Learning Models. Preprints 2023, 2023100952. https://doi.org/10.20944/preprints202310.0952.v1
Santos, D. Predicting the Severity of Hepatitis C Using Machine Learning Models. Preprints2023, 2023100952. https://doi.org/10.20944/preprints202310.0952.v1
APA Style
Santos, D. (2023). Predicting the Severity of Hepatitis C Using Machine Learning Models. Preprints. https://doi.org/10.20944/preprints202310.0952.v1
Chicago/Turabian Style
Santos, D. 2023 "Predicting the Severity of Hepatitis C Using Machine Learning Models" Preprints. https://doi.org/10.20944/preprints202310.0952.v1
Abstract
Hepatitis C presents a significant global health challenge, necessitating early diagnosis and precise severity classification for timely medical intervention. This study explores the application of machine learning techniques to predict the severity of hepatitis C, leveraging an extensive dataset. Our approach encompasses rigorous data preprocessing, advanced model development, and fine-tuned hyperparameter optimization to ensure accurate and reliable predictions. We evaluated four classification models: Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM), comparing their accuracy in classifying patients. The results showed that the Random Forest and Gradient Boosting models outperformed the others with an accuracy of approximately 93.50%, demonstrating their potential in assisting Hepatitis C diagnosis. Further model enhancements through hyperparameter tuning and feature engineering can improve the precision of Hepatitis C diagnosis, contributing to better patient care.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
