Saudi Cultural Missions Theses & Dissertations
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Item Restricted Predicting Comorbidities Using Electronic Health Records: The Role of Genetics and Explainable Artificial Intelligence(Saudi Digital Library, 2026) Alsaleh, Mohanad; Thygesen, Johan; Honghan, Wu; Andrew, McQuillinBackground: Comorbidity, the coexistence of multiple conditions in one individual, complicates care, reduces quality of life, and increases costs. This thesis examines whether medically actionable genes, as defined by the American College of Medical Genetics and Genomics (ACMG), are associated with additional comorbidities. Identifying such links, particularly when incidental pathogenic variants are discovered, could inform clinical action, guide patient management, and improve outcomes. Materials and methods: A systematic review evaluated machine learning (ML) models for comorbidity prediction, with emphasis on performance and explainability. A phenome-wide association study (PheWAS) using data from Genomics England (n = 78,121) examined associations between pathogenic variants in 81 ACMG genes and 301 comorbidities. Finally, SHAP, an explainable AI method, was applied to interpret genetic, clinical, and demographic drivers of comorbidity predictions. Results: The systematic review covered 22 studies describing 61 ML models. While 52 models showed good performance (accuracy 70–95% and AUC 0.70–0.89), only five incorporated explainability. The PheWAS identified 102 significant associations between 32 ACMG genes and 49 comorbidities, confirming known findings (TSC2 with acute kidney injury) and suggesting novel ones (TTR with intellectual disability). For ML prediction, XGBoost achieved the best performance (AUC = 0.93) and was used for SHAP analysis. SHAP highlighted established contributions, such as TTN to cardiovascular disease, and novel findings, including RYR1 with neonatal sepsis. Age and sex also played important roles across multiple comorbidity predictions. Discussion and conclusion: These findings expand the understanding of the impact of pathogenic variants in ACMG genes, highlighting broader comorbidity associations and demonstrating the value of XAI for interpreting prediction drivers. Limitations, including small sample sizes and extreme data imbalance, contributed to poor model performance and led to the exclusion of some genes and diseases. Future work should validate the findings in larger, independent cohorts and address challenges related to imbalance.12 0