Radwa Elshawi

TL;DR: The utility of various model-agnostic explanation techniques of machine learning models with a case study for analyzing the outcomes of the machine learning random forest model for predicting the individuals at risk of developing hypertension based on cardiorespiratory fitness data is demonstrated.

TL;DR: It is shown that machine learning methods provide accurate prediction of in-hospital LOS for cardiac patients and can be used in clinical bed management and resources allocation.

TL;DR: This work analyzes in details the building blocks of the software stack for supporting big data science as a commodity service for data scientists.

TL;DR: This paper presents a comprehensive experimental evaluation of three recent and popular local model agnostic interpretability techniques, namely, LIME, SHAP and Anchors on different types of real-world healthcare data and shows that LIME achieves the lowest performance for the identity metric and the highestperformance for the separability metric across all datasets included in this study.

TL;DR: The RTF model has shown the best performance and outperformed all other machine learning techniques examined in this study and has also shown that it is critical to carefully explore and evaluate the performance of the machine learning models using various model evaluation methods as the prediction accuracy can significantly differ.