A Study towards Supervised Learning Techniques for a Well-Predictive Modelling Utilising Electronic Health Record Data
Abhay Bhatia1, Rajeev Kumar2, Golnoosh Manteghi3
1Dr. Abhay Bhatia, Post Doctoral Researcher, Kuala Lumpur University of Science & Technology (KLUST), Malaysia, Jalan Ikram-Uniten (Kajang),
2Prof. (Dr.) Rajeev Kumar, Professor, Department of Computer Science & Engineering, Moradabad Institute of Technology, Moradabad, Uttar Pradesh, India.
3Dr. Golnoosh Manteghi, Department of Architecture and Built Environment, Kuala Lumpur University of Science & Technology (KLUST), Malaysia, Jalan Ikram-Uniten (Kajang),
Manuscript received on 30 September 2025 | Revised Manuscript received on 06 October 2025 | Manuscript Accepted on 15 October 2025 | Manuscript published on 30 October 2025 | PP: 1-5 | Volume-13 Issue-11, October 2025 | Retrieval Number: 100.1/ijese.L262013121125 | DOI: 10.35940/ijese.L2620.13111025
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: EHR, X-Rays, CT-Scan, MRI, Health, NLP
Scope of the Article: Artificial Intelligence and Methods