Hepatitis C virus (HCV) detection is a critical aspect of early intervention and effective management of the disease. This paper presents a comprehensive study focused on enhancing the detection accuracy of HCV through the integration of advanced techniques - SMOTE, Optuna, and SHAP - alongside extensive exploratory data analysis (EDA). The study addresses class imbalance using Synthetic Minority Over-sampling Technique (SMOTE), optimizes model performance with Optuna for hyperparameter tuning, and provides model interpretability using SHAP (SHapley Additive exPlanations). EDA is leveraged to gain valuable insights into the dataset's characteristics, ensuring robust data preprocessing and feature engineering. The results show 97% improved HCV detection performance, highlighting the efficacy of the proposed methodology in medical diagnostics and aiding healthcare professionals in making informed clinical decisions.

This research used modern machine learning ways to predict the stages of primary biliary cholangitis using data from the Mayo Clinic trial. The research aims to obtain high prediction accuracy while representing balanced evaluation metrics. Important techniques include automated hyperparameters optimization with Optuna and Recursive Feature Elimination to improve model performance. Pre-processing included handling missing values, encoding of categorical features, and addressing class imbalances using SMOTE. A total of twelve machine learning algorithms are evaluated with ensemble-based models such as CatBoost and Extra Trees producing much better results. Evaluation metrics take into account all model predictions, including accuracy, precision, recall, F1 score, and ROC-AUC for performing balanced and interpretative evaluations of performances critical for imbalanced datasets. This endeavor includes clinical and laboratory information illustrating the prospect of machine learning in advancing therapeutic diagnosis, emphasizing the rigor and robustness in evaluation laid groundwork for future research to encompass even more generalizable and robust diagnostic tools.

The main contribution of the present study is to develop a novel hybrid machine learning model to enhance the defect prediction in industrial manufacturing processes. In this work, the model integrated four base models of XGBoost, LightGBM, CatBoost, and an artificial network, whose features are modeled with Random Forest (RF) as the metamodel using a stacking ensemble approach. For this study, the industrial data from Kaggle were used, and for their extensive and detailed hyperparameter optimization with Optuna, we greatly improved the prediction performance with the model. In the context of this study, key challenges like the data imbalance and the selection of the important features were solved using data balancing techniques like SMOTE and random forest-based analysis for selecting the most important input features. The hybrid model generated great results, which were quite better than the traditional single models, with an accuracy of 96.06% and precision, recall, and F1 scores of 95.10%, 97.32%, and 96.20%, respectively. The real-world applications of this model can be many by accurately and timely predicting defects in industrial environments. All results are reliable and interpretable due to the usage of robust data preprocessing methods, including feature standardization and correlation analysis. This study's results will have a significant impact on such tasks as defect management in manufacturing, as it provides a very scalable solution to enhance product quality, minimize operational cost, and improve process efficiency. This research illustrates the promise of hybrid machine learning methods in tooling manufacturing process optimization and the performance of industry.