Predicting vitamin D deficiency using optimized random forest classifier
Clinical Nutrition ESPEN VOL 60, P1-10 https://doi.org/10.1016/j.clnesp.2023.12.146 PDF is behind a paywall
Aladeen Alloubani. Belal Abuhaija, M. Almatari. Ghaith Jaradat, Baha Ihnaini
Appears that age is the primary predictor (image via Google)
Background
Vitamin D can be acquired from various dietary sources, but exposure to sunlight's ultraviolet rays can convert a natural compound called ergosterol present in the skin into vitamin D.
Aim
The current study aimed to investigate vital parameters and use an optimized random forest (OptRF) classifier to understand better and predict the effect of environmental and nutritional factors of Vitamin D deficiency.
Methods
A predictive, cross-sectional, and correlational design was utilized in a study involving 350 male and female Tabuk citizens in Saudi Arabia. The Weka machine-learning tool was employed for comprehensive data analysis, with the OptRF algorithm being tailored through advanced feature selection methods and meticulous hyperparameter tuning.
Results
In addition to the OptRF classifier, a number of traditional machine learning techniques have been tested and compared on the dataset of vitamin D to analyze and build the predictive model for classifying vitamin D deficiency. In general, the OptRF-based predictive model can statistically describe data for determining significant features related to Vitamin D deficiency. OptRF demonstrated its ability to classify vitamin D deficiency cases with high accuracy 91.42 %.
Conclusion
This study showed that Tabuk citizens are at high risk of vitamin D deficiency especially among females (gender predictor) with little regard to age, income, smoking, and sun exposure. In addition,
- exercise,
- less Vitamin D intake, and
- less intake of Calcium
are also predictors of Vitamin D deficiency. Due to the link between Vitamin D Deficiency and major chronic illnesses, it is important to emphasize the importance of identifying risk factors and screening for Vitamin D Deficiency. It may be appropriate for nutritionists, nurses, and physicians to promote community awareness about strategies to improve dietary Vitamin D intake or consider recommending supplements.
A few of the references
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- Vitamin D deficiency, excessive gestational weight gain, and oxidative stress predict small for gestational age newborns using an artificial neural network model.– . 2022; 11: 574
- Oral health in breast cancer women with vitamin D deficiency: A machine learning study.– J Clin Med. 2022; 11: 4662
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- Predictive ability of machine-learning methods for vitamin D deficiency prediction by anthropometric parameters. – . 2022; 10: 616
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VitaminDWiki – Predict Vitamin D category contains:
It is very difficult to predict the response to supplementation of Vitamin D, or additional sun/UV
There are a huge number of factors involved.
This page also has studies predicting deficiency without Vitamin D tests
- Vitamin D deficiency predicted with 91% accuracy ( AI, age, paywall) - April 2024
- Predictors of low vitamin D: race, age, and BMI (confirmed now by Machine Learning) – Feb 2024
- Low Vitamin D screening with just 5 questions (for less than 12 ng) – June 2022
- Top 10 signs of Vitamin D Deficiency (9 minute Video) - Oct 2021
- Estimate Vitamin D levels based on questionnaires (12 studies) – July 2020
- Is a senior Vitamin D insufficient - a 2 minute questionnaire is 85 percent accurate – Nov 2019
- Simple Vitamin D deficiency scoring system – Feb 2016
- Toward predicting vitamin D levels without a blood test. by VitaminDWiki
- Excellent prediction of very low vitamin D in elderly from just 16 questions (analyzed by ML) – June 2017
- Quick, free, self test of vitamin D deficiency 90% chance <20 ng
VitaminDWiki – 13 reasons why many seniors need more vitamin D (both dose and level) - July 2023
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