Predicting osteoarthritis in adults using statistical data mining and machine learning

Background: Osteoarthritis (OA) has traditionally been considered a disease of older adults (⩾65 years old), but it may appear in younger adults. However, the risk factors for OA in younger adults need to be further evaluated. Objectives: To develop a prediction model for identifying risk factors of...

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Vydáno v:	Therapeutic advances in musculoskeletal disease Ročník 14; s. 1759720X221104935
Hlavní autoři:	Bertoncelli, Carlo M., Altamura, Paola, Bagui, Sikha, Bagui, Subhash, Vieira, Edgar Ramos, Costantini, Stefania, Monticone, Marco, Solla, Federico, Bertoncelli, Domenico
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London, England SAGE Publications 2022 SAGE PUBLICATIONS, INC SAGE Publishing
Témata:	Arthritis Blood pressure Body mass index Ethnicity Machine learning Musculoskeletal diseases Original Research Osteoarthritis Risk factors statistical data mining arthritis machine learning osteoarthritis
ISSN:	1759-720X, 1759-7218
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Abstract	Background: Osteoarthritis (OA) has traditionally been considered a disease of older adults (⩾65 years old), but it may appear in younger adults. However, the risk factors for OA in younger adults need to be further evaluated. Objectives: To develop a prediction model for identifying risk factors of OA in subjects aged 20–50 years and compare the performance of different machine learning models. Methods: We included data from 52,512 participants of the National Health and Nutrition Examination Survey; of those, we analyzed only subjects aged 20–50 years (n = 19,133), with or without OA. The supervised machine learning model ‘Deep PredictMed’ based on logistic regression, deep neural network (DNN), and support vector machine was used for identifying demographic and personal characteristics that are associated with OA. Finally, we compared the performance of the different models. Results: Being a female (p < 0.001), older age (p < 0.001), a smoker (p < 0.001), higher body mass index (p < 0.001), high blood pressure (p < 0.001), race/ethnicity (lowest risk among Mexican Americans, p = 0.01), and physical and mental limitations (p < 0.001) were associated with having OA. Best predictive performance yielded a 75% area under the receiver operating characteristic curve. Conclusion: Sex (female), age (older), smoking (yes), body mass index (higher), blood pressure (high), race/ethnicity, and physical and mental limitations are risk factors for having OA in adults aged 20–50 years. The best predictive performance was achieved using DNN algorithms.
AbstractList	Osteoarthritis (OA) has traditionally been considered a disease of older adults (⩾65 years old), but it may appear in younger adults. However, the risk factors for OA in younger adults need to be further evaluated.BackgroundOsteoarthritis (OA) has traditionally been considered a disease of older adults (⩾65 years old), but it may appear in younger adults. However, the risk factors for OA in younger adults need to be further evaluated.To develop a prediction model for identifying risk factors of OA in subjects aged 20-50 years and compare the performance of different machine learning models.ObjectivesTo develop a prediction model for identifying risk factors of OA in subjects aged 20-50 years and compare the performance of different machine learning models.We included data from 52,512 participants of the National Health and Nutrition Examination Survey; of those, we analyzed only subjects aged 20-50 years (n = 19,133), with or without OA. The supervised machine learning model 'Deep PredictMed' based on logistic regression, deep neural network (DNN), and support vector machine was used for identifying demographic and personal characteristics that are associated with OA. Finally, we compared the performance of the different models.MethodsWe included data from 52,512 participants of the National Health and Nutrition Examination Survey; of those, we analyzed only subjects aged 20-50 years (n = 19,133), with or without OA. The supervised machine learning model 'Deep PredictMed' based on logistic regression, deep neural network (DNN), and support vector machine was used for identifying demographic and personal characteristics that are associated with OA. Finally, we compared the performance of the different models.Being a female (p < 0.001), older age (p < 0.001), a smoker (p < 0.001), higher body mass index (p < 0.001), high blood pressure (p < 0.001), race/ethnicity (lowest risk among Mexican Americans, p = 0.01), and physical and mental limitations (p < 0.001) were associated with having OA. Best predictive performance yielded a 75% area under the receiver operating characteristic curve.ResultsBeing a female (p < 0.001), older age (p < 0.001), a smoker (p < 0.001), higher body mass index (p < 0.001), high blood pressure (p < 0.001), race/ethnicity (lowest risk among Mexican Americans, p = 0.01), and physical and mental limitations (p < 0.001) were associated with having OA. Best predictive performance yielded a 75% area under the receiver operating characteristic curve.Sex (female), age (older), smoking (yes), body mass index (higher), blood pressure (high), race/ethnicity, and physical and mental limitations are risk factors for having OA in adults aged 20-50 years. The best predictive performance was achieved using DNN algorithms.ConclusionSex (female), age (older), smoking (yes), body mass index (higher), blood pressure (high), race/ethnicity, and physical and mental limitations are risk factors for having OA in adults aged 20-50 years. The best predictive performance was achieved using DNN algorithms. Background: Osteoarthritis (OA) has traditionally been considered a disease of older adults (⩾65 years old), but it may appear in younger adults. However, the risk factors for OA in younger adults need to be further evaluated. Objectives: To develop a prediction model for identifying risk factors of OA in subjects aged 20–50 years and compare the performance of different machine learning models. Methods: We included data from 52,512 participants of the National Health and Nutrition Examination Survey; of those, we analyzed only subjects aged 20–50 years (n = 19,133), with or without OA. The supervised machine learning model ‘Deep PredictMed’ based on logistic regression, deep neural network (DNN), and support vector machine was used for identifying demographic and personal characteristics that are associated with OA. Finally, we compared the performance of the different models. Results: Being a female (p < 0.001), older age (p < 0.001), a smoker (p < 0.001), higher body mass index (p < 0.001), high blood pressure (p < 0.001), race/ethnicity (lowest risk among Mexican Americans, p = 0.01), and physical and mental limitations (p < 0.001) were associated with having OA. Best predictive performance yielded a 75% area under the receiver operating characteristic curve. Conclusion: Sex (female), age (older), smoking (yes), body mass index (higher), blood pressure (high), race/ethnicity, and physical and mental limitations are risk factors for having OA in adults aged 20–50 years. The best predictive performance was achieved using DNN algorithms. Background: Osteoarthritis (OA) has traditionally been considered a disease of older adults (⩾65 years old), but it may appear in younger adults. However, the risk factors for OA in younger adults need to be further evaluated. Objectives: To develop a prediction model for identifying risk factors of OA in subjects aged 20–50 years and compare the performance of different machine learning models. Methods: We included data from 52,512 participants of the National Health and Nutrition Examination Survey; of those, we analyzed only subjects aged 20–50 years (n = 19,133), with or without OA. The supervised machine learning model ‘Deep PredictMed’ based on logistic regression, deep neural network (DNN), and support vector machine was used for identifying demographic and personal characteristics that are associated with OA. Finally, we compared the performance of the different models. Results: Being a female (p < 0.001), older age (p < 0.001), a smoker (p < 0.001), higher body mass index (p < 0.001), high blood pressure (p < 0.001), race/ethnicity (lowest risk among Mexican Americans, p = 0.01), and physical and mental limitations (p < 0.001) were associated with having OA. Best predictive performance yielded a 75% area under the receiver operating characteristic curve. Conclusion: Sex (female), age (older), smoking (yes), body mass index (higher), blood pressure (high), race/ethnicity, and physical and mental limitations are risk factors for having OA in adults aged 20–50 years. The best predictive performance was achieved using DNN algorithms.
Author	Solla, Federico Bagui, Subhash Altamura, Paola Vieira, Edgar Ramos Monticone, Marco Costantini, Stefania Bertoncelli, Domenico Bertoncelli, Carlo M. Bagui, Sikha
Author_xml	– sequence: 1 givenname: Carlo M. orcidid: 0000-0002-9689-0209 surname: Bertoncelli fullname: Bertoncelli, Carlo M. email: bertoncelli@unice.fr organization: Department of Information Engineering Computer Science and Mathematics, University of L’Aquila, L’Aquila, Italy – sequence: 2 givenname: Paola surname: Altamura fullname: Altamura, Paola organization: Department of Medicinal Chemistry and Pharmaceutical Technology, University of Chieti, Chieti, Italy – sequence: 3 givenname: Sikha surname: Bagui fullname: Bagui, Sikha organization: Department of Computer Science, Hal Marcus College of Science and Engineering, University of West Florida, Pensacola, FL, USA – sequence: 4 givenname: Subhash surname: Bagui fullname: Bagui, Subhash organization: Department of Computer Science, Hal Marcus College of Science and Engineering, University of West Florida, Pensacola, FL, USA – sequence: 5 givenname: Edgar Ramos surname: Vieira fullname: Vieira, Edgar Ramos organization: Department of Physical Therapy, Florida International University, Miami, FL, USA – sequence: 6 givenname: Stefania surname: Costantini fullname: Costantini, Stefania organization: Department of Information Engineering Computer Science and Mathematics, University of L’Aquila, L’Aquila, Italy – sequence: 7 givenname: Marco surname: Monticone fullname: Monticone, Marco organization: Neurorehabilitation Unit, Department of Neuroscience and Rehabilitation, G. Brotzu Hospital, University of Cagliari, Cagliari, Italy – sequence: 8 givenname: Federico surname: Solla fullname: Solla, Federico organization: Department of Pediatric Orthopaedic Surgery, Lenval University Pediatric Hospital of Nice, Nice, France – sequence: 9 givenname: Domenico surname: Bertoncelli fullname: Bertoncelli, Domenico email: bertoncelli@unice.fr organization: Department of Information Engineering Computer Science and Mathematics, University of L’Aquila, L’Aquila, Italy
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Keywords	statistical data mining arthritis machine learning osteoarthritis
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Snippet	Background: Osteoarthritis (OA) has traditionally been considered a disease of older adults (⩾65 years old), but it may appear in younger adults. However, the... Background: Osteoarthritis (OA) has traditionally been considered a disease of older adults (⩾65 years old), but it may appear in younger adults. However, the... Osteoarthritis (OA) has traditionally been considered a disease of older adults (⩾65 years old), but it may appear in younger adults. However, the risk factors...
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SubjectTerms	Arthritis Blood pressure Body mass index Ethnicity Machine learning Musculoskeletal diseases Original Research Osteoarthritis Risk factors
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Title	Predicting osteoarthritis in adults using statistical data mining and machine learning
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