Clinical nutrition ESPEN · 2023
Predicting vitamin D deficiency using optimized random forest classifier.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Alloubani Aladeen, Abuhaija Belal, Almatari M, Jaradat Ghaith, Ihnaini Baha
Аннотация
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.
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.