Junaid Minhas, Jamee C Mcbride
Perceptions of Mental Health Professionals on Nutritional Psychiatry as an Adjunct Treatment in Mainstream Psychiatric Settings in New South Wales, Australia
Cureus
background chronic hepatitis c hcv infection presents global health challenges significant morbidity mortality implications successfully treating patients cirrhosis may lead mortality rates comparable general population study aims utilize machine learning techniques create predictive mortality models individuals chronic hcv infections methods data chronic hcv patients sultan qaboos university hospital 20092017 underwent analysis data preprocessing handled missing values scaled features using python via anaconda model training involved selectkbest feature selection algorithms logistic regression random forest gradient boosting svm evaluation included diverse metrics 5fold crossvalidation ensuring consistent performance assessment results cohort 702 patients meeting eligibility criteria predominantly male median age 47 analyzed across followup period 974 months survival probabilities 12 36 120 months 900 840 730 respectively ten key features selected mortality prediction included hemoglobin levels alanine aminotransferase comorbidities hcv genotype coinfections followup duration treatment response machine learning models including logistic regression random forest gradient boosting support vector machine models showed high discriminatory power logistic regression consistently achieving auc value 0929 factors associated increased mortality risk included cardiovascular diseases coinfections failure achieve svr lower alt levels specific hcv genotypes linked better survival outcomes conclusions study presents use machine learning models predict mortality chronic hcv patients providing crucial insights risk assessment tailored treatments validation refinement models essential enhance clinical utility optimize patient care improve outcomes individuals chronic hcv infections