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A Machine Learning-Based Mortality Prediction Model for Patients with Chronic Hepatitis C Infection: An Exploratory
Abdullah M Al Alawi1,2, Halima H Al Shuaili3, Khalid Al-Naamani3
1Department of Medicine, Sultan Qaboos University Hospital, Muscat 123, Oman.
Insights
Machine learning models accurately predict mortality risk in chronic hepatitis C (HCV) patients. Key factors like hemoglobin and comorbidities inform survival outcomes, aiding personalized treatment strategies.
Area of Science:
- Hepatology and viral gastroenterology
- Medical informatics and machine learning
- Public health and epidemiology
Background:
- Chronic hepatitis C virus (HCV) infection poses significant global health challenges, impacting morbidity and mortality.
- Effective treatment of HCV cirrhosis can normalize mortality rates, highlighting the need for precise risk stratification.
- Predictive modeling for HCV patient mortality is crucial for optimizing clinical management.
Purpose of the Study:
- To develop and evaluate machine learning models for predicting mortality in chronic HCV patients.
- To identify key clinical and demographic factors associated with mortality risk in this cohort.
- To enhance risk assessment and inform personalized treatment strategies for chronic HCV.
Main Methods:
- Analysis of a cohort of 702 chronic HCV patients from Sultan Qaboos University Hospital (2009-2017).
- Data pre-processing, feature selection (SelectKBest), and training of machine learning algorithms (logistic regression, random forest, gradient boosting, SVM).
- Performance evaluation using 5-fold cross-validation and metrics including Area Under the Curve (AUC).
Main Results:
- Survival probabilities at 12, 36, and 120 months were 90.0%, 84.0%, and 73.0%, respectively.
- Logistic regression achieved a high AUC of 0.929, demonstrating strong predictive power.
- Identified predictors of mortality included hemoglobin, ALT, comorbidities, HCV genotype, coinfections, follow-up duration, and treatment response.
Conclusions:
- Machine learning models effectively predict mortality in chronic HCV patients.
- Key factors influencing mortality risk were identified, enabling better patient stratification.
- These models offer valuable insights for clinical decision-making and improving patient outcomes in HCV management.
Abstract:
Background: Chronic hepatitis C (HCV) infection presents global health challenges with significant morbidity and mortality implications. Successfully treating patients with cirrhosis may lead to mortality rates comparable to the general population. This study aims to utilize machine learning techniques to create predictive mortality models for individuals with chronic HCV infections. Methods: Data from chronic HCV patients at Sultan Qaboos University Hospital (2009-2017) underwent analysis. Data pre-processing handled missing values and scaled features using Python via Anaconda. Model training involved SelectKBest feature selection and algorithms such as logistic regression, random forest, gradient boosting, and SVM. The evaluation included diverse metrics, with 5-fold cross-validation, ensuring consistent performance assessment. Results: A cohort of 702 patients meeting the eligibility criteria, predominantly male, with a median age of 47, was analyzed across a follow-up period of 97.4 months. Survival probabilities at 12, 36, and 120 months were 90.0%, 84.0%, and 73.0%, respectively. Ten key features selected for mortality prediction included hemoglobin levels, alanine aminotransferase, comorbidities, HCV genotype, coinfections, follow-up duration, and treatment response. Machine learning models, including the logistic regression, random forest, gradient boosting, and support vector machine models, showed high discriminatory power, with logistic regression consistently achieving an AUC value of 0.929. Factors associated with increased mortality risk included cardiovascular diseases, coinfections, and failure to achieve a SVR, while lower ALT levels and specific HCV genotypes were linked to better survival outcomes. Conclusions: This study presents the use of machine learning models to predict mortality in chronic HCV patients, providing crucial insights for risk assessment and tailored treatments. Further validation and refinement of these models are essential to enhance their clinical utility, optimize patient care, and improve outcomes for individuals with chronic HCV infections.
