- author = "Fatma Hilal Yagin and Radwa {El Shawi} and Abdulmohsen Algarni and Cemil Colak and Fahaid Al-Hashem and Luca Paolo Ardigo",
- title = "Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating {AutoML} and Explainable Artificial Intelligence",
- journal = "Diagnostics",
- year = "2024",
- volume = "14",
- number = "18",
- pages = "article no 2049",
- keywords = "genetic algorithms, genetic programming, TPOT, autoML, AI-driven diagnostics, AutoML in healthcare, XAI, metabolomics biomarkers, hepatocellular carcinoma detection, TreeSHAP interpretability",
- pubmedid = "39335728",
- ISSN = "2075-4418",
-
URL = "
https://www.mdpi.com/2075-4418/14/18/2049",
-
DOI = "
10.3390/diagnostics14182049",
- size = "16 pages",
-
abstract = "Background: This study aims to assess the efficacy of
combining automated machine learning (AutoML) and
explainable artificial intelligence (XAI) in
identifying metabolomic biomarkers that can
differentiate between hepatocellular carcinoma (HCC)
and liver cirrhosis in patients with hepatitis C virus
(HCV) infection.
Methods: We investigated publicly accessible data encompassing HCC patients and cirrhotic controls. The TPOT tool, which is an AutoML tool, was used to optimize the preparation of features and data, as well as to select the most suitable machine learning model. The TreeSHAP approach, which is a type of XAI, was used to interpret the model by assessing each metabolite individual contribution to the categorization process.
Results: TPOT had superior performance in distinguishing between HCC and cirrhosis compared to other AutoML approaches AutoSKlearn and H2O AutoML, in addition to traditional machine learning models such as random forest, support vector machine, and k-nearest neighbor. The TPOT technique attained an AUC value of 0.81, showcasing superior accuracy, sensitivity, and specificity in comparison to the other models. Key metabolites, including L-valine, glycine, and DL-isoleucine, were identified as essential by TPOT and subsequently verified by TreeSHAP analysis. TreeSHAP provided a comprehensive explanation of the contribution of these metabolites to the model predictions, thereby increasing the interpretability and dependability of the results. This thorough assessment highlights the strength and reliability of the AutoML framework in the development of clinical biomarkers.
Conclusions: This study shows that AutoML and XAI can be used together to create metabolomic biomarkers that are specific to HCC. The exceptional performance of TPOT in comparison to traditional models highlights its capacity to identify biomarkers. Furthermore, TreeSHAP boosted model transparency by highlighting the relevance of certain metabolites. This comprehensive method has the potential to enhance the identification of biomarkers and generate precise, easily understandable, AI-driven solutions for diagnosing HCC.",
-
notes = "also known as
\cite{diagnostics14182049}
https://www.mdpi.com/journal/diagnostics",
