Proposing an Effective Deep Learning Model for Vitamin D Deficiency Diagnosis

Document Type

Conference Proceeding

Source of Publication

Proceedings - IEEE Symposium on Computer-Based Medical Systems

Publication Date

1-1-2024

Abstract

Following the declaration of the COVID-19 pandemic by the World Health Organization, the importance of Vitamin D and the incidence of Vitamin D Deficiency (VDD) have emerged as serious worldwide healthconcerns. There is an increasing demand for noninvasive prediction approaches for determining the severity of VDD. This work collected primary data on serum Vitamin D levels from a benchmark dataset and used machine learning classifiers and categorical data encoding to predict VDD. It introduces a deep learning strategy that uses a Bidirectional Long Short-Term Memory (Bi-LSTM) model, which is improved by balancing the dataset, to better predict VDD severity. Originally intended to predict VDD level, the Bi-LSTM model beat previous models in predicting the severity of VDD, with 95% accuracy, 95% precision, 96% recall, and 95% F1 score. Our methodology appreciably raises the capacity to correctly recognize vitamin D deficiency when compared to earlier methods.

ISBN

[9798350384727]

ISSN

1063-7125

Publisher

IEEE

First Page

51

Last Page

56

Disciplines

Computer Sciences

Keywords

Vitamin D Deficiency, Deep Learning, Bi-LSTM model, Machine learning classifiers, VDD severity

Scopus ID

85200484504

Indexed in Scopus

yes

Open Access

no

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