Machine learning based analysis for intellectual disability in Down syndrome
Down syndrome (DS) or trisomy 21 is the most common genetic cause of intellectual disability (ID), but a pathogenic mechanism has not been identified yet. Studying a complex and not monogenic condition such as DS, a clear correlation between cause and effect might be difficult to find through classi...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023066525 |
| _version_ | 1797669856809582592 |
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| author | Federico Baldo Allison Piovesan Marijana Rakvin Giuseppe Ramacieri Chiara Locatelli Silvia Lanfranchi Sara Onnivello Francesca Pulina Maria Caracausi Francesca Antonaros Michele Lombardi Maria Chiara Pelleri |
| author_facet | Federico Baldo Allison Piovesan Marijana Rakvin Giuseppe Ramacieri Chiara Locatelli Silvia Lanfranchi Sara Onnivello Francesca Pulina Maria Caracausi Francesca Antonaros Michele Lombardi Maria Chiara Pelleri |
| author_sort | Federico Baldo |
| collection | DOAJ |
| description | Down syndrome (DS) or trisomy 21 is the most common genetic cause of intellectual disability (ID), but a pathogenic mechanism has not been identified yet. Studying a complex and not monogenic condition such as DS, a clear correlation between cause and effect might be difficult to find through classical analysis methods, thus different approaches need to be used. The increased availability of big data has made the use of artificial intelligence (AI) and in particular machine learning (ML) in the medical field possible.The purpose of this work is the application of ML techniques to provide an analysis of clinical records obtained from subjects with DS and study their association with ID.We have applied two tree-based ML models (random forest and gradient boosting machine) to the research question: how to identify key features likely associated with ID in DS. We analyzed 109 features (or variables) in 106 DS subjects. The outcome of the analysis was the age equivalent (AE) score as indicator of intellectual functioning, impaired in ID. We applied several methods to configure the models: feature selection through Boruta framework to minimize random correlation; data augmentation to overcome the issue of a small dataset; age effect mitigation to take into account the chronological age of the subjects.The results show that ML algorithms can be applied with good accuracy to identify variables likely involved in cognitive impairment in DS. In particular, we show how random forest and gradient boosting machine produce results with low error (MSE <0.12) and an acceptable R2 (0.70 and 0.93). Interestingly, the ranking of the variables point to several features of interest related to hearing, gastrointestinal alterations, thyroid state, immune system and vitamin B12 that can be considered with particular attention for improving care pathways for people with DS.In conclusion, ML-based model may assist researchers in identifying key features likely correlated with ID in DS, and ultimately, may improve research efforts focused on the identification of possible therapeutic targets and new care pathways. We believe this study can be the basis for further testing/validating of our algorithms with multiple and larger datasets. |
| first_indexed | 2024-03-11T20:50:52Z |
| format | Article |
| id | doaj.art-aa3b2ca581af40cb90f81a1529311590 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-11T20:50:52Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-aa3b2ca581af40cb90f81a15293115902023-10-01T05:59:40ZengElsevierHeliyon2405-84402023-09-0199e19444Machine learning based analysis for intellectual disability in Down syndromeFederico Baldo0Allison Piovesan1Marijana Rakvin2Giuseppe Ramacieri3Chiara Locatelli4Silvia Lanfranchi5Sara Onnivello6Francesca Pulina7Maria Caracausi8Francesca Antonaros9Michele Lombardi10Maria Chiara Pelleri11Department of Computer Science and Engineering, University of Bologna, Viale Risorgimento 2, 40136, Bologna, BO, ItalyDepartment of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Via Massarenti 9, 40138, Bologna, BO, ItalyDepartment of Computer Science and Engineering, University of Bologna, Viale Risorgimento 2, 40136, Bologna, BO, ItalyDepartment of Medical and Surgical Sciences (DIMEC), University of Bologna, Via Massarenti 9, 40138, Bologna, BO, ItalyNeonatology Unit, IRCCS University General Hospital Sant’Orsola Polyclinic, Via Massarenti 9, 40138, Bologna, BO, ItalyDepartment of Developmental Psychology and Socialisation, University of Padova, Via Venezia 8, 35131, Padua, PD, ItalyDepartment of Developmental Psychology and Socialisation, University of Padova, Via Venezia 8, 35131, Padua, PD, ItalyDepartment of Developmental Psychology and Socialisation, University of Padova, Via Venezia 8, 35131, Padua, PD, ItalyDepartment of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Via Massarenti 9, 40138, Bologna, BO, ItalyDepartment of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Via Massarenti 9, 40138, Bologna, BO, ItalyDepartment of Computer Science and Engineering, University of Bologna, Viale Risorgimento 2, 40136, Bologna, BO, Italy; Corresponding author. Department of Computer Science and Engineering, University of Bologna, Viale Risorgimento 2, 40136, Bologna, BO, Italy.Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Via Massarenti 9, 40138, Bologna, BO, Italy; Corresponding author.Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, via Massarenti, 9, 40138, Bologna, BO, Italy.Down syndrome (DS) or trisomy 21 is the most common genetic cause of intellectual disability (ID), but a pathogenic mechanism has not been identified yet. Studying a complex and not monogenic condition such as DS, a clear correlation between cause and effect might be difficult to find through classical analysis methods, thus different approaches need to be used. The increased availability of big data has made the use of artificial intelligence (AI) and in particular machine learning (ML) in the medical field possible.The purpose of this work is the application of ML techniques to provide an analysis of clinical records obtained from subjects with DS and study their association with ID.We have applied two tree-based ML models (random forest and gradient boosting machine) to the research question: how to identify key features likely associated with ID in DS. We analyzed 109 features (or variables) in 106 DS subjects. The outcome of the analysis was the age equivalent (AE) score as indicator of intellectual functioning, impaired in ID. We applied several methods to configure the models: feature selection through Boruta framework to minimize random correlation; data augmentation to overcome the issue of a small dataset; age effect mitigation to take into account the chronological age of the subjects.The results show that ML algorithms can be applied with good accuracy to identify variables likely involved in cognitive impairment in DS. In particular, we show how random forest and gradient boosting machine produce results with low error (MSE <0.12) and an acceptable R2 (0.70 and 0.93). Interestingly, the ranking of the variables point to several features of interest related to hearing, gastrointestinal alterations, thyroid state, immune system and vitamin B12 that can be considered with particular attention for improving care pathways for people with DS.In conclusion, ML-based model may assist researchers in identifying key features likely correlated with ID in DS, and ultimately, may improve research efforts focused on the identification of possible therapeutic targets and new care pathways. We believe this study can be the basis for further testing/validating of our algorithms with multiple and larger datasets.http://www.sciencedirect.com/science/article/pii/S2405844023066525Down syndromeIntellectual disabilityData miningMachine learning |
| spellingShingle | Federico Baldo Allison Piovesan Marijana Rakvin Giuseppe Ramacieri Chiara Locatelli Silvia Lanfranchi Sara Onnivello Francesca Pulina Maria Caracausi Francesca Antonaros Michele Lombardi Maria Chiara Pelleri Machine learning based analysis for intellectual disability in Down syndrome Heliyon Down syndrome Intellectual disability Data mining Machine learning |
| title | Machine learning based analysis for intellectual disability in Down syndrome |
| title_full | Machine learning based analysis for intellectual disability in Down syndrome |
| title_fullStr | Machine learning based analysis for intellectual disability in Down syndrome |
| title_full_unstemmed | Machine learning based analysis for intellectual disability in Down syndrome |
| title_short | Machine learning based analysis for intellectual disability in Down syndrome |
| title_sort | machine learning based analysis for intellectual disability in down syndrome |
| topic | Down syndrome Intellectual disability Data mining Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023066525 |
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