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Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization.

Usharani Bhimavarapu¹, Gopi Battineni^2,3, Nalini Chintalapudi²

¹Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India.

Bioengineering (Basel, Switzerland)

|February 26, 2025

Summary

Predicting vitamin D deficiency (VDD) severity is crucial. This study developed a machine learning model using improved whale optimization for accurate, non-invasive VDD detection, achieving 99.4% accuracy.

Keywords:

performance metrics stacking classifier vitamin D whale optimization

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Area of Science:

Biomedical Informatics
Machine Learning Applications
Nutritional Science

Background:

Vitamin D deficiency (VDD) poses global health risks.
Standard 25-hydroxy vitamin D (25-OH-D) blood tests are often impractical.
Non-invasive prediction of VDD severity is increasingly needed.

Purpose of the Study:

Develop a clinically acceptable machine learning (ML) model for VDD detection.
Eliminate the need for 25-OH-D blood tests.
Address model overfitting and enhance multi-class prediction.

Main Methods:

Applied data reduction, cleaning, and transformation to the vitamin D dataset.
Utilized the improved whale optimization (IWOA) algorithm for feature selection and weight function optimization.
Employed a stacking classifier for enhanced prediction accuracy.

Main Results:

Achieved a high accuracy of 99.4% in VDD detection.
The proposed IWOA algorithm demonstrated superior performance in feature selection.
The stacking classifier proved effective and robust compared to other models.

Conclusions:

The developed ML model offers a promising non-invasive approach for VDD assessment.
Advanced optimization techniques significantly improve prediction accuracy.
The stacking classifier is a robust choice for detecting vitamin D deficiencies.