Sean Miller
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Sean Miller has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Curtis Busby-Earle
The University of the West Indies Mona
1 shared publication - Keaton Logan
Department of Applied Sciences, Faculty of Engineering and Applied Technology, Caribbean Maritime University, Main Campus, Palisadoes Park, Norman Manley Highway, Kingston P.O. Box 8081, Jamaica
1 shared publication - Lisa-Dionne Morris
Leeds University
1 shared publication - Patricia E. Cowell
The University of Sheffield
1 shared publication - Ricardo Anderson
The University of the West Indies Mona
1 shared publication
Latest Publications
- Open Access
- 21 August 2025
- Published by Springer Nature in Research Square
Multi-Perspective Machine Learning MPML: A High-Performance and Interpretable Ensemble Method for Heart Disease Prediction
Machine Learning (ML) has demonstrated strong predictive capabilities in healthcare, often surpassing human performance in pattern recognition and decision-making. However, many high-performing models lack interpretability, which is critical in clinical settings where understanding and trusting predictions is essential. To address this, we introduce Multi-Perspective Machine Learning (MPML)—an ensemble approach that integrates multiple complementary techniques to form a high-performing, interpretable model. MPML organizes features into meaningful subsets, or perspectives, enabling both global and instance-level interpretability. Unlike traditional ensemble methods such as Bagging, Boosting, and Random Forest, MPML delivers significantly higher-quality predictions across all evaluation metrics while maintaining a transparent structure. Applied to a heart disease dataset, MPML not only improves predictive accuracy but also provides detailed, accessible explanations for individual patient outcomes, advancing the potential for practical and ethical deployment of ML in healthcare.
- 21 August 2025
- Published by Springer Nature in Research Square
Open AccessMulti-Perspective Machine Learning MPML: A High-Performance and Interpretable Ensemble Method for Heart Disease Prediction
Machine Learning (ML) has demonstrated strong predictive capabilities in healthcare, often surpassing human performance in pattern recognition and decision-making. However, many high-performing models lack interpretability, which is critical in clinical settings where understanding and trusting predictions is essential. To address this, we introduce Multi-Perspective Machine Learning (MPML)—an ensemble approach that integrates multiple complementary techniques to form a high-performing, interpretable model. MPML organizes features into meaningful subsets, or perspectives, enabling both global and instance-level interpretability. Unlike traditional ensemble methods such as Bagging, Boosting, and Random Forest, MPML delivers significantly higher-quality predictions across all evaluation metrics while maintaining a transparent structure. Applied to a heart disease dataset, MPML not only improves predictive accuracy but also provides detailed, accessible explanations for individual patient outcomes, advancing the potential for practical and ethical deployment of ML in healthcare.