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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2022-07-01
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| Series: | Therapeutic Advances in Musculoskeletal Disease |
| Online Access: | https://doi.org/10.1177/1759720X221104935 |
| _version_ | 1811293879536713728 |
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| author | Carlo M. Bertoncelli Paola Altamura Sikha Bagui Subhash Bagui Edgar Ramos Vieira Stefania Costantini Marco Monticone Federico Solla Domenico Bertoncelli |
| author_facet | Carlo M. Bertoncelli Paola Altamura Sikha Bagui Subhash Bagui Edgar Ramos Vieira Stefania Costantini Marco Monticone Federico Solla Domenico Bertoncelli |
| author_sort | Carlo M. Bertoncelli |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T05:08:08Z |
| format | Article |
| id | doaj.art-579d9744472149519ee66a56f8048bdb |
| institution | Directory Open Access Journal |
| issn | 1759-7218 |
| language | English |
| last_indexed | 2024-04-13T05:08:08Z |
| publishDate | 2022-07-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Therapeutic Advances in Musculoskeletal Disease |
| spelling | doaj.art-579d9744472149519ee66a56f8048bdb2022-12-22T03:01:06ZengSAGE PublishingTherapeutic Advances in Musculoskeletal Disease1759-72182022-07-011410.1177/1759720X221104935Predicting osteoarthritis in adults using statistical data mining and machine learningCarlo M. BertoncelliPaola AltamuraSikha BaguiSubhash BaguiEdgar Ramos VieiraStefania CostantiniMarco MonticoneFederico SollaDomenico BertoncelliBackground: 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.https://doi.org/10.1177/1759720X221104935 |
| spellingShingle | Carlo M. Bertoncelli Paola Altamura Sikha Bagui Subhash Bagui Edgar Ramos Vieira Stefania Costantini Marco Monticone Federico Solla Domenico Bertoncelli Predicting osteoarthritis in adults using statistical data mining and machine learning Therapeutic Advances in Musculoskeletal Disease |
| title | Predicting osteoarthritis in adults using statistical data mining and machine learning |
| title_full | Predicting osteoarthritis in adults using statistical data mining and machine learning |
| title_fullStr | Predicting osteoarthritis in adults using statistical data mining and machine learning |
| title_full_unstemmed | Predicting osteoarthritis in adults using statistical data mining and machine learning |
| title_short | Predicting osteoarthritis in adults using statistical data mining and machine learning |
| title_sort | predicting osteoarthritis in adults using statistical data mining and machine learning |
| url | https://doi.org/10.1177/1759720X221104935 |
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