Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning Model Enabled with Data Optimisation for Prediction of Coronary Heart Disease Subject The topic of the resource Accuracy; Classification Algorithms; Coronary Heart Disease; Cross-Validation; Data Preprocessing; Feature Engineering; Machine Learning; Model Generalization; Prediction; Risk Assessment Description An account of the resource Cardiovascular disorders remain leading cause for mortality worldwide, necessitating robust early risk assessment. Although machine learning models show promise, most rely on conventional preprocessing, which lacks model portability across datasets. We propose an integrated preprocessing pipeline enhancing model generalizability. Our methodology standardises features solely based on training statistics and then transforms test data identically to prevent leakage. We handle class imbalance through synchronised oversampling, enabling consistent performance despite distribution shifts. This framework was evaluated on an open-source dataset of clinical parameters from an African cohort using classifiers like support vector machines and gradient boosting. All models achieved upto 80% accuracy. Remarkably, evaluating the identical models on five external European and Asian datasets maintains 80% - 86% accuracy. Our reproducible data conditioning strategy enables precise and transportable heart disease risk prediction, overcoming population variability. The framework provides the flexibility to readily retrain models on new data or update risk algorithms for clinical implementation in diverse locales. Our work accelerates the safe translation of machine learning to guide cardiovascular screening worldwide. 2024 IEEE. Creator An entity primarily responsible for making the resource Kanth P.C.; Vijayalakshmi S.; Palathara T.S. Source A related resource from which the described resource is derived TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/TQCEBT59414.2024.10545194" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545194</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204476908&amp;doi=10.1109%2FTQCEBT59414.2024.10545194&amp;partnerID=40&amp;md5=6eea7c45b36395074ef643eba4c6a2c6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204476908&amp;doi=10.1109%2fTQCEBT59414.2024.10545194&amp;partnerID=40&amp;md5=6eea7c45b36395074ef643eba4c6a2c6</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038427-7 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kanth P.C., Christ University, Department of Data Science, India; Vijayalakshmi S., Christ University, Department of Data Science, India; Palathara T.S., Christ University, Department of Data Science, India