Medicine

This project compares LIME and SHAP for explaining heart disease predictions using the UCI Heart Disease dataset. Several models were built from scratch, with XGBoost achieving the highest accuracy (83.3%). While SHAP produced stable, theory-based feature attributions, LIME offered intuitive, instance-level insights that benefited from an aggregated approach to improve consistency. Together, they highlight how combining interpretability tools can make machine learning in healthcare both accurate and transparent.

Biomedical image analysis plays a critical role in the diagnosis and treatment of patients with serious diseases. Advances in data availability and machine learning have improved the accuracy and efficiency of these analyses. This paper explores the use of convolutional neural networks (CNNs) for blood cell classification, a task essential for complete blood counts that identify infections based on blood cell characteristics. Manual assessment of blood cells is error-prone and resource-intensive, highlighting the need for automated solutions. We implemented a CNN using the BloodMNIST dataset, which contains over 17,000 images across eight blood cell classes. The CNN achieved superior performance with an accuracy of 0.914, significantly outperforming the baseline 1-layer Feedforward Neural Network on all evaluated metrics. Our results demonstrate that CNNs, with their ability to capture spatial hierarchies, provide a robust solution for blood cell classification, offering improved accuracy and reliability for medical diagnostics.