Capturing and Predicting Patients’ Outcomes Using Machine Learning Algorithms

Abstract

The rapid growth of digital health technologies has transformed how hospitals collect and store patient information. From electronic health records and laboratory results to vital signs and diagnostic reports, healthcare systems now generate vast amounts of data every day. Despite this progress, many healthcare providers still rely heavily on traditional statistical tools and clinical judgment when predicting patient outcomes such as recovery, complications, readmission, or mortality. These conventional approaches often struggle to fully capture the complex relationships that exist within medical data. This study introduces a practical and data-driven approach to capturing and predicting patient outcomes using machine learning algorithms. The new idea behind this research is the integration of both static patient information (such as age, gender, and medical history) and dynamic clinical indicators (such as changes in vital signs and laboratory trends over time) into a unified predictive framework. Rather than relying on a single algorithm, the study combines multiple supervised machine learning models within an ensemble structure to improve prediction accuracy and reliability. The methodology follows a quantitative experimental design using structured hospital datasets. Data preprocessing techniques are applied to handle missing values, encode categorical variables, and normalize numerical features. Relevant clinical features are selected to reduce noise and improve model performance. Several machine learning algorithms including Logistic Regression, Random Forest, Support Vector Machine, Gradient Boosting, and Artificial Neural Networks are trained and evaluated using k-fold cross-validation. Model performance is assessed using standard evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC, while hyperparameter tuning is used to optimize results. The proposed framework is expected to enhance early risk detection and support clinical decision making. By providing more accurate and timely predictions, the system can assist healthcare professionals in identifying high-risk patients earlier, planning targeted interventions, reducing readmission rates, and ultimately improving overall patient care outcomes.