Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization
There is a growing need to predict the severity of vitamin D deficiency (VDD) through non-invasive methods due to its significant global health concerns. For vitamin D-level assessments, the 25-hydroxy vitamin D (25-OH-D) blood test is the standard, but it is often not a practical test. This study i...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/12/2/200 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849719736501272576 |
|---|---|
| author | Usharani Bhimavarapu Gopi Battineni Nalini Chintalapudi |
| author_facet | Usharani Bhimavarapu Gopi Battineni Nalini Chintalapudi |
| author_sort | Usharani Bhimavarapu |
| collection | DOAJ |
| description | There is a growing need to predict the severity of vitamin D deficiency (VDD) through non-invasive methods due to its significant global health concerns. For vitamin D-level assessments, the 25-hydroxy vitamin D (25-OH-D) blood test is the standard, but it is often not a practical test. This study is focused on developing a machine learning (ML) model that is clinically acceptable for accurately detecting vitamin D status and eliminates the need for 25-OH-D determination while addressing overfitting. To enhance the capacity of the classification system to predict multiple classes, preprocessing procedures such as data reduction, cleaning, and transformation were used on the raw vitamin D dataset. The improved whale optimization (IWOA) algorithm was used for feature selection, which optimized weight functions to improve prediction accuracy. To gauge the effectiveness of the proposed IWOA algorithm, evaluation metrics like precision, accuracy, recall, and F1-score were used. The results showed a 99.4% accuracy, demonstrating that the proposed method outperformed the others. A comparative analysis demonstrated that the stacking classifier was the superior choice over the other classifiers, highlighting its effectiveness and robustness in detecting deficiencies. Incorporating advanced optimization techniques, the proposed method’s promise for generating accurate predictions is highlighted in the study. |
| format | Article |
| id | doaj-art-0e4abd4d55294a2da8d11e08365300a5 |
| institution | DOAJ |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-0e4abd4d55294a2da8d11e08365300a52025-08-20T03:12:05ZengMDPI AGBioengineering2306-53542025-02-0112220010.3390/bioengineering12020200Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid OptimizationUsharani Bhimavarapu0Gopi Battineni1Nalini Chintalapudi2Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, IndiaClinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, ItalyClinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, ItalyThere is a growing need to predict the severity of vitamin D deficiency (VDD) through non-invasive methods due to its significant global health concerns. For vitamin D-level assessments, the 25-hydroxy vitamin D (25-OH-D) blood test is the standard, but it is often not a practical test. This study is focused on developing a machine learning (ML) model that is clinically acceptable for accurately detecting vitamin D status and eliminates the need for 25-OH-D determination while addressing overfitting. To enhance the capacity of the classification system to predict multiple classes, preprocessing procedures such as data reduction, cleaning, and transformation were used on the raw vitamin D dataset. The improved whale optimization (IWOA) algorithm was used for feature selection, which optimized weight functions to improve prediction accuracy. To gauge the effectiveness of the proposed IWOA algorithm, evaluation metrics like precision, accuracy, recall, and F1-score were used. The results showed a 99.4% accuracy, demonstrating that the proposed method outperformed the others. A comparative analysis demonstrated that the stacking classifier was the superior choice over the other classifiers, highlighting its effectiveness and robustness in detecting deficiencies. Incorporating advanced optimization techniques, the proposed method’s promise for generating accurate predictions is highlighted in the study.https://www.mdpi.com/2306-5354/12/2/200vitamin Dstacking classifierwhale optimizationperformance metrics |
| spellingShingle | Usharani Bhimavarapu Gopi Battineni Nalini Chintalapudi Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization Bioengineering vitamin D stacking classifier whale optimization performance metrics |
| title | Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization |
| title_full | Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization |
| title_fullStr | Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization |
| title_full_unstemmed | Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization |
| title_short | Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization |
| title_sort | machine learning driven prediction of vitamin d deficiency severity with hybrid optimization |
| topic | vitamin D stacking classifier whale optimization performance metrics |
| url | https://www.mdpi.com/2306-5354/12/2/200 |
| work_keys_str_mv | AT usharanibhimavarapu machinelearningdrivenpredictionofvitaminddeficiencyseveritywithhybridoptimization AT gopibattineni machinelearningdrivenpredictionofvitaminddeficiencyseveritywithhybridoptimization AT nalinichintalapudi machinelearningdrivenpredictionofvitaminddeficiencyseveritywithhybridoptimization |