EXplainable AI for Decision Support to Obesity Comorbidities Diagnosis
This paper describes the implementation of a comprehensive clinical decision support system (CDSS) for the risk factors prediction of comorbidities related to obesity and for the characterization of indirect connections between such comorbidities and non-communicable diseases. In particular, the dir...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10265033/ |
| _version_ | 1797663076902764544 |
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| author | Grazia V. Aiosa Maurizio Palesi Francesca Sapuppo |
| author_facet | Grazia V. Aiosa Maurizio Palesi Francesca Sapuppo |
| author_sort | Grazia V. Aiosa |
| collection | DOAJ |
| description | This paper describes the implementation of a comprehensive clinical decision support system (CDSS) for the risk factors prediction of comorbidities related to obesity and for the characterization of indirect connections between such comorbidities and non-communicable diseases. In particular, the direct correlation between obesity, diabetes, cardiovascular, and heart disease is analyzed by using machine learning (ML) predictive models, while the connection of the co-occurring disorders to the numerous additional non-communicable diseases is analyzed via a graph-based user interface. The CDSS here proposed is, therefore, structured with three main components: ML predictive models based on publicly available datasets, explainable artificial intelligence (XAI) local and global model interpretation, and graph-based representation of non-communicable disease connections. Multiple ML models are presented for risk assessment and a comparison is carried out based on performance key performance indicators. The best-performing model for each disease was proved to be: the multi-layer perceptron for diabetes and heart disease, and extreme gradient boosting for cardiovascular disease. Comorbidities risk factor prediction and a XAI local model explanation is performed on significant case studies. In addition, XAI global model interpretation is given for the entire dataset providing insights on the features’ contribution to the models’ implementation. Moreover, the graph-based visualization of indirect disease co-occurrence is performed by filtering connections according to different relative risk factor thresholds. This interface can be exploited by healthcare professionals to obtain, according to the needs and the clinical approach, a global perspective on obesity and its associated pathologies prevention as well as long-term treatment and care provision. |
| first_indexed | 2024-03-11T19:09:19Z |
| format | Article |
| id | doaj.art-91bb75a42f3d4c7a9fdf733c92239a31 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T19:09:19Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-91bb75a42f3d4c7a9fdf733c92239a312023-10-09T23:01:25ZengIEEEIEEE Access2169-35362023-01-011110776710778210.1109/ACCESS.2023.332005710265033EXplainable AI for Decision Support to Obesity Comorbidities DiagnosisGrazia V. Aiosa0Maurizio Palesi1Francesca Sapuppo2https://orcid.org/0000-0001-8772-2759Dipartimento di Ingegneria Elettrica, Elettronica ed Informatica, Università degli Studi di Catania, Catania, ItalyDipartimento di Ingegneria, Università degli Studi di Messina, Messina, ItalyDipartimento di Ingegneria Elettrica, Elettronica ed Informatica, Università degli Studi di Catania, Catania, ItalyThis paper describes the implementation of a comprehensive clinical decision support system (CDSS) for the risk factors prediction of comorbidities related to obesity and for the characterization of indirect connections between such comorbidities and non-communicable diseases. In particular, the direct correlation between obesity, diabetes, cardiovascular, and heart disease is analyzed by using machine learning (ML) predictive models, while the connection of the co-occurring disorders to the numerous additional non-communicable diseases is analyzed via a graph-based user interface. The CDSS here proposed is, therefore, structured with three main components: ML predictive models based on publicly available datasets, explainable artificial intelligence (XAI) local and global model interpretation, and graph-based representation of non-communicable disease connections. Multiple ML models are presented for risk assessment and a comparison is carried out based on performance key performance indicators. The best-performing model for each disease was proved to be: the multi-layer perceptron for diabetes and heart disease, and extreme gradient boosting for cardiovascular disease. Comorbidities risk factor prediction and a XAI local model explanation is performed on significant case studies. In addition, XAI global model interpretation is given for the entire dataset providing insights on the features’ contribution to the models’ implementation. Moreover, the graph-based visualization of indirect disease co-occurrence is performed by filtering connections according to different relative risk factor thresholds. This interface can be exploited by healthcare professionals to obtain, according to the needs and the clinical approach, a global perspective on obesity and its associated pathologies prevention as well as long-term treatment and care provision.https://ieeexplore.ieee.org/document/10265033/Clinical decision support systemexplainable artificial intelligencemulti-node graphmachine learningobesity comorbiditypredictive models |
| spellingShingle | Grazia V. Aiosa Maurizio Palesi Francesca Sapuppo EXplainable AI for Decision Support to Obesity Comorbidities Diagnosis IEEE Access Clinical decision support system explainable artificial intelligence multi-node graph machine learning obesity comorbidity predictive models |
| title | EXplainable AI for Decision Support to Obesity Comorbidities Diagnosis |
| title_full | EXplainable AI for Decision Support to Obesity Comorbidities Diagnosis |
| title_fullStr | EXplainable AI for Decision Support to Obesity Comorbidities Diagnosis |
| title_full_unstemmed | EXplainable AI for Decision Support to Obesity Comorbidities Diagnosis |
| title_short | EXplainable AI for Decision Support to Obesity Comorbidities Diagnosis |
| title_sort | explainable ai for decision support to obesity comorbidities diagnosis |
| topic | Clinical decision support system explainable artificial intelligence multi-node graph machine learning obesity comorbidity predictive models |
| url | https://ieeexplore.ieee.org/document/10265033/ |
| work_keys_str_mv | AT graziavaiosa explainableaifordecisionsupporttoobesitycomorbiditiesdiagnosis AT mauriziopalesi explainableaifordecisionsupporttoobesitycomorbiditiesdiagnosis AT francescasapuppo explainableaifordecisionsupporttoobesitycomorbiditiesdiagnosis |