Randomized Explainable Machine Learning Models for Efficient Medical Diagnosis

Abstract

Deep learning-based models have revolutionized medical diagnostics by using Big Data to enhance disease diagnosis and clinical decision-making. However, their significant computational demands and opaque decision-making processes, often characterized as "black-box" systems, pose major challenges in time-critical and resource-constrained healthcare settings. To address these issues, this study explores the application of randomized machine learning models, specifically Extreme Learning Machines (ELMs) and Random Vector Functional Link (RVFL) networks, in medical diagnostics. These models introduce stochasticity into their training processes, reducing computational complexity and training times while maintaining accuracy. Furthermore, we integrate Explainable AI techniques namely Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) to explain the decision-making rationale of ELMs and RVFL. Performance evaluations on genitourinary cancers and coronary artery disease datasets demonstrate that RVFL outperforms traditional deep learning models, achieving superior accuracy of 88.29% with a computational overhead of 6.22 seconds for genitourinary cancers, and an accuracy of 81.64% with a computational time of 0.0308 seconds for coronary artery disease. This research highlights the potential of randomized models in enhancing efficiency and transparency in medical diagnosis, thereby accelerating better treatment outcomes and advocating for more accessible and interpretable AI solutions in healthcare.

Topics & Concepts