VITA-D: A Radiomic Web Tool for Predicting Vitamin D Deficiency Levels
Background: Vitamin D deficiency is a significant risk factor for several chronic conditions. This study aims to predict vitamin D deficiency levels in a private database, collected from the southern part of Loja-Ecuador using a graphical web interface tool based on artificial intelligence algorithm...
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MDPI AG
2025-02-01
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| author | Yuliana Jiménez-Gaona Oscar Vivanco-Galván Darwin Castillo-Malla Israel Vivanco-Gualán Patricia Díaz-Guzmán |
| author_facet | Yuliana Jiménez-Gaona Oscar Vivanco-Galván Darwin Castillo-Malla Israel Vivanco-Gualán Patricia Díaz-Guzmán |
| author_sort | Yuliana Jiménez-Gaona |
| collection | DOAJ |
| description | Background: Vitamin D deficiency is a significant risk factor for several chronic conditions. This study aims to predict vitamin D deficiency levels in a private database, collected from the southern part of Loja-Ecuador using a graphical web interface tool based on artificial intelligence algorithms. Methods: Two databases were processed using ML training models: SVM, Random Forest (RF), Linear Regression (LR). (i) Private data collection was undertaken on 465 patients from a local university, where vitamin D levels were measured through a blood sample collection to calculate the concentration of 25-hydroxy vitamin D in plasma and determine it by enzyme-linked immunosorbent assay, and (ii) public data collection was obtained from the FigShare database. Then, a survey was conducted from April 2022 to June 2023, identifying 157 variables, 18 of which were used for ML training models. Results: Vitamin D deficiency levels in private patients reached 18.10 ng/mL and 20.42 ng/mL in the public. The RF algorithm achieved (87.73%) accuracy, the SVM (80.0%), and LR (70.70%). RF was selected as the best performance model for web application design in binary levels classification: deficiency (Class 0) indicates vitamin D levels below 15 ng/mL, and sufficiency (Class 1) indicates vitamin D levels above 15 ng/mL. Conclusions: The “VITA-D” web application was used to monitor and predict vitamin D levels and deficiency factor risk based on clinical and sociodemographic data, providing an efficient and cost-effective alternative to traditional vitamin D testing methods. |
| format | Article |
| id | doaj-art-c41675ff69ab408582117d23ca3572ed |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c41675ff69ab408582117d23ca3572ed2025-08-20T03:12:04ZengMDPI AGApplied Sciences2076-34172025-02-01154179810.3390/app15041798VITA-D: A Radiomic Web Tool for Predicting Vitamin D Deficiency LevelsYuliana Jiménez-Gaona0Oscar Vivanco-Galván1Darwin Castillo-Malla2Israel Vivanco-Gualán3Patricia Díaz-Guzmán4Departamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n CP 1101608, EcuadorDepartamento de Ciencias Biológicas y Agropecuarias, Universidad Técnica Particular de Loja, San Cayetano Alto s/n CP 1101608, EcuadorDepartamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n CP 1101608, EcuadorDepartamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n CP 1101608, EcuadorDepartamento de Ciencias Médicas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n, Loja 11-01-608, EcuadorBackground: Vitamin D deficiency is a significant risk factor for several chronic conditions. This study aims to predict vitamin D deficiency levels in a private database, collected from the southern part of Loja-Ecuador using a graphical web interface tool based on artificial intelligence algorithms. Methods: Two databases were processed using ML training models: SVM, Random Forest (RF), Linear Regression (LR). (i) Private data collection was undertaken on 465 patients from a local university, where vitamin D levels were measured through a blood sample collection to calculate the concentration of 25-hydroxy vitamin D in plasma and determine it by enzyme-linked immunosorbent assay, and (ii) public data collection was obtained from the FigShare database. Then, a survey was conducted from April 2022 to June 2023, identifying 157 variables, 18 of which were used for ML training models. Results: Vitamin D deficiency levels in private patients reached 18.10 ng/mL and 20.42 ng/mL in the public. The RF algorithm achieved (87.73%) accuracy, the SVM (80.0%), and LR (70.70%). RF was selected as the best performance model for web application design in binary levels classification: deficiency (Class 0) indicates vitamin D levels below 15 ng/mL, and sufficiency (Class 1) indicates vitamin D levels above 15 ng/mL. Conclusions: The “VITA-D” web application was used to monitor and predict vitamin D levels and deficiency factor risk based on clinical and sociodemographic data, providing an efficient and cost-effective alternative to traditional vitamin D testing methods.https://www.mdpi.com/2076-3417/15/4/1798vitamin D deficiency prediction toolclinical data analysis with machine learningdeficiencyhypovitaminosis Dmachine learningprediction models |
| spellingShingle | Yuliana Jiménez-Gaona Oscar Vivanco-Galván Darwin Castillo-Malla Israel Vivanco-Gualán Patricia Díaz-Guzmán VITA-D: A Radiomic Web Tool for Predicting Vitamin D Deficiency Levels Applied Sciences vitamin D deficiency prediction tool clinical data analysis with machine learning deficiency hypovitaminosis D machine learning prediction models |
| title | VITA-D: A Radiomic Web Tool for Predicting Vitamin D Deficiency Levels |
| title_full | VITA-D: A Radiomic Web Tool for Predicting Vitamin D Deficiency Levels |
| title_fullStr | VITA-D: A Radiomic Web Tool for Predicting Vitamin D Deficiency Levels |
| title_full_unstemmed | VITA-D: A Radiomic Web Tool for Predicting Vitamin D Deficiency Levels |
| title_short | VITA-D: A Radiomic Web Tool for Predicting Vitamin D Deficiency Levels |
| title_sort | vita d a radiomic web tool for predicting vitamin d deficiency levels |
| topic | vitamin D deficiency prediction tool clinical data analysis with machine learning deficiency hypovitaminosis D machine learning prediction models |
| url | https://www.mdpi.com/2076-3417/15/4/1798 |
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