Extracting Knowledge from Data in Lightweight Digital Twin Construction

Abstract

In the medical domain, the early detection and monitoring of specific diseases require both accuracy and interpretability to support clinical decisions. Human digital twin systems are increasingly used in this context, but their adoption often requires data-intensive process for the learning tasks and a high degree of explainability to ensure clinical reliability. To address these challenges, we propose a pipeline that prioritises lightweight and explainability, to extract actionable knowledge from patient data in terms of rules. The approach follows a traditional Machine Learning methodology, including data pre-processing, followed by the construction and validation of a classification model. A rule extraction phase is then introduced to make the classifier’s decision process interpretable. The reliability of the pipeline was evaluated by extracting decision rules for the detection of kidney damage in patients with Congenital Solitary Functioning Kidney. Through the analysis of patient data and the use of a Random Forest classifier, key clinical parameters (e.g., creatinine levels, Holter monitor measurements, and kidney volume) were identified as fundamental to support accurate and reliable diagnoses in clinical practice.

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