A Data-Driven Comparative Study of Machine Learning Algorithms for Hepatitis C Diagnosis

Hepatitis C is a viral infectious disease caused that affects 58 million people globally making it a public health crisis, and it is reported that 20-30% of cases develop to severe liver disease. This creates the need for data-driven based method that such as Machine learning that depends on data inputs in making accurate outputs with high precision. This study explores the use of Machine learning algorithms in optimizing the detection of hepatitis C status of patients. This research utilizes a publicly available dataset that contains 12 explanatory features and one target feature that addresses the patient’s diagnosis status. Data preprocessing techniques such as checking of missing values, assessment for possible outliers and Exploratory data analysis was carried on the dataset to improve the quality of the dataset for predictive analysis. The EDA results reviews that majority of the features had significant relationship (p-value < 0.05) with the target variable. Also, we observed the distribution of the disease diagnosis by gender reviews that females showed higher prevalence, and when we consider age, older people are more likely to contract the HCV. This result aligns with research findings from recent studies. The dataset was scaled and separated using a ratio split of 80:20 (80% of data for training). We applied SMOTE algorithm to the training data to balance the diagnosis distribution of the target variable. The model was trained using a wide parameter space using a cross-validation fold of 10 to select the best parameters. The ML models evaluated in this research include Logistic Regression, Support Vector Machine, Random Forest, XGBOOST, Decision Tree, and Extra Trees. The model performance metrics shows that XGBOOST was superior with an accuracy score of 98%, followed by Random Forest (97%), Decision Tree (94%), Extra Trees (93%), SVM (92%), and Logistic Regression (87%). The Kappa statistics for XGBOOST which was estimated as 0.8827 shows that difference between the predicted instances and the actual instances was minimal. The performance metrics of the XGBOOST was also contrast with ML algorithms trained by recent researchers in detecting HCV and it shows our model was more precise underscoring the significance of datapreprocessing, feature-scaling, SMOTE and hyperparameter tuning in predictive analysis in healthcare.