Chronic Apical Periodontitis Risk Prediction and Assessment

Workflow and Process: Sample Selection and Preparation: Objective: Select relevant CAP-related samples from the UK Biobank ensuring quality and representativeness. Tasks: Integrate personal health information, blood biochemical indicators, and genetic data to construct feature sets. Related Files/Directories: S1_sample_selection.csv: CSV file with selected samples. feature_info: Directory containing information about features used. Data Integration and Feature Engineering: Objective: Combine genetic and electronic medical record data to build comprehensive feature sets. Tasks: Process and integrate various data types to prepare for model development. Related Files/Directories: data_integration: Directory containing scripts and data files for integration. Machine Learning Model Development: Objective: Develop and train various machine learning models, including random forest, support vector machine, logistic regression, and adaptive boosting, using the integrated data. Tasks: Implement and train models to predict CAP risk based on the selected features. Related Files/Directories: code: Directory containing code files for machine learning model development. models: Directory with trained model files. Model Performance Evaluation: Objective: Assess the performance of the machine learning models and compare their predictive capabilities. Tasks: Evaluate models using metrics such as area under the curve (AUC) and sensitivity. Related Files/Directories: S2_traintest_id.RData: RData file with training and testing identifiers. S4_model_performance.RData: RData file with model performance results. S5_model_res.zip: Compressed file containing detailed model results