Statistical Modeling for Predictive Healthcare Analytics

Abstract

Statistical modeling is crucial in predictive healthcare analytics, utilizing sensor data to forecast outcomes and enhance patient care. Key techniques include machine learning algorithms like decision trees, random forests, and neural networks for risk assessment and treatment prediction. Logistic regression and survival analysis models handle binary outcomes and time-to-event data, respectively. Evaluation metrics like accuracy and AUC-ROC gauge model performance. Techniques for imbalanced data, feature selection, and model interpretability bolster predictive robustness. Bayesian modeling integrates prior knowledge, improving reliability and interpretability. Statistical modeling empowers data-driven clinical decisions, enhancing patient outcomes and personalized medicine.