Advised by Dr. Fadi Alsaleem

The fusion of engineering, medicine, and data science offers transformative solutions to healthcare challenges. This dissertation epitomizes this synergy by focusing on developing novel diagnosis methods for Peripheral Artery Disease (PAD), a condition often underdiagnosed and demanding specialized training for accurate diagnosis and treatment. In this work, we first employed machine learning (ML) to classify individuals with PAD using laboratory-based gait features. Next, we delved into the significance of GRF as a diagnostic tool for PAD severity. Furthermore, we introduced a 2D health map and the 1D GRF Propulsive Peak scale, presented transformative tools for PAD diagnosis. These tools could enhance PAD severity assessment precision and hint at innovations like wearable technologies to potentially transition PAD management from clinical to home settings. All in all, this dissertation underscores the potential of ML in PAD diagnosis, severity assessment, and treatment outcome prediction. The findings advocate a paradigm shift towards a data-driven, patient-centric approach to PAD management, integrating ML insights with emerging technologies for improved patient outcomes and tailored treatment strategies.