Enhancing coronary heart disease diagnosis: Comparative analysis of data pre-processing techniques and machine learning models using clinical medical records.

Abstract

Machine learning techniques offer significant potential for improving the diagnosis of coronary heart disease by enabling earlier detection and timely intervention. This study presents a machine learning-based method utilizing clinical records to evaluate the impact of different data preprocessing sequences on predictive accuracy. Two clinical datasets were examined: one comprising heart failure patient data with 14 clinical features, and the Cleveland Heart Disease Dataset. The investigation compared two preprocessing strategies: standardisation prior to balancing, and balancing prior to scaling. Six machine learning models (XGBoost, GBDT, AdaBoost, Random Forest, KNN, and RaSE) were trained on an 80:20 data split and assessed using accuracy, precision, recall, and F1-score. Hyperparameters were optimized with Bayesian Optimisation. Results showed that both preprocessing designs achieved perfect accuracy on the Cleveland dataset. For the heart failure dataset, balancing before scaling led to improved accuracy (95%) compared with standardising before balancing (93.33%), and yielded higher macro-average and weighted-average F1-scores, signifying better overall classification performance. Among the evaluated models, XGBoost consistently provided the most robust predictions across conditions. These findings highlight the critical influence of preprocessing sequence on model effectiveness in imbalanced clinical data and suggest that balancing before scaling significantly enhances classification accuracy. XGBoost stands out as a reliable model for potential implementation in clinical decision support systems. Overall, this study advances the development of AI-driven tools for digital health applications, contributing meaningful insights to the field of health informatics.