A Study towards Supervised Learning Techniques for a Well-Predictive Modelling Utilising Electronic Health Record Data

Electronic health records (EHRs) provide a substantial repository of structured and unstructured data, enabling predictive modelling for medical research and clinical decision-making. The gathered EHR data is more useful for machine learning; all the information about a diagnosed patient — such as their lab results, demographics, treatments, etc. — needs to be compiled, cleaned, and converted to a standard format for use. To start supervised learning, split the dataset into two sets: a training set and a test set. Then a model that works well is chosen, such as decision trees, logistic regression, random forests, or neural networks. People use these models to learn about diseases, the risks they pose, and the best treatments. To assess how well something works, model evaluation uses metrics such as precision and accuracy. We can learn more about patient care, achieve better results, and make medical associations more evidence-based by systematically applying supervised learning techniques to EHR data.