Applying Advanced Computational Models to Optimise Electronic Health Records and Enhance Clinical Decision-Making
DOI:
https://doi.org/10.48047/HM.V9.I2.2023.Keywords:
Electronic Health Records (EHRs), Machine Learning in Healthcare, Clinical Decision Support Systems (CDSS), Cardiovascular Disease Prediction, Random Forest, XGBoost, LSTM, Predictive Analytics in MedicineAbstract
Electronic Health Records (EHRs) have become a cornerstone in modern healthcare, providing structured patient data that can significantly enhance clinical decision-making. However, the manual interpretation of large-scale EHR data presents challenges in efficiency and accuracy, necessitating the integration of advanced computational models. This study explores machine learning and deep learning techniques to optimise EHR-based cardiovascular disease (CVD) prediction, leveraging data-driven insights to enhance patient risk stratification. The Cardiovascular Disease Dataset from Kaggle, comprising 70,000 patient records, was utilised to train and evaluate three models: Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks. Among the three models, XGBoost demonstrated the highest predictive accuracy at 73.5%, making it the most effective model for CVD detection. LSTM exhibited the highest recall (0.81), making it well-suited for identifying high-risk patients, but it also generated a higher number of false positives, potentially leading to unnecessary medical interventions. Random Forest, a baseline model, achieved 71.2% accuracy, showing stable but slightly lower performance. These findings highlight the superiority of XGBoost in predictive accuracy, while LSTM remains useful in maximising sensitivity to disease detection. The results emphasise the potential of machine learning in automated cardiovascular risk assessment, allowing for data-driven clinical decision-making that can enhance early intervention strategies. The study's implications extend to EHR optimisation, AI integration in medical workflows, and the deployment of computational models for clinical risk management.
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