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INTELLIGENT HEALTHCARE DATA ANALYTICS COUPLED WITH SENSOR ASSESSMENT FOR NON-ALCOHOLIC FATTY LIVER DISEASE (NAFLD)
This research was conducted to develop and evaluate a screening tool for Hepatic Steatosis (or fatty liver) detection using machine learning based models. The developed models are intended to be used as a potential clinical decision support tool for identifying patients with Non-Alcoholic Fatty Liver Disease (NAFLD). Two versions of a HS prediction tool are discussed in Paper 1, Objectives 1A, and 1B, respectively.
Explainability analysis of the developed models is also a major component of this work, discussed in Paper 2. Models from Paper 1 are analyzed further for interpretability and the results are then compared with current clinical literature. Insights from the explainability analysis are used to identify best models that follow the clinical literature logically. Most contributing features within each model are also identified in this work.
Another aspect of NAFLD management is related to the chronic exposure to heavy metals in the environment (such as: Arsenic, Lead, Cadmium etc.). The heavy metal exposure component is explored in two ways in this dissertation. In paper 3, another version of the ML-based screening tool is explored by including heavy metal exposure data. The results from the model (with heavy metal data) are then compared with models that exclude the heavy metal exposure data. The results and their implications are discussed in paper 3.
Arsenic is a major hepatotoxin and the chronic exposure can lead to severe liver injury. In Paper 4, a commercially available Arsenic detection kit was examined for Arsenic detection in water at a household level. The kit was evaluated following a short experimental plan and the obtained results are discussed. Finally, the obtained images were quantified digitally using a customized image analysis and pattern recognition algorithm. The methods used for quantification and the obtained results are also discussed.