A Predictive Performance Analysis of Vitamin D Deficiency Using a Decision Tree model

Background: VD is involved in various pathophysiological mechanisms in a plethora of diseases. And also, there is a strong demand for the prediction of its severity using different methods. The study aims to evaluate performance of DT as one of the machine learning models in the prediction of severi...

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Autor principal:	F. Osmani
Formato:	article
Lenguaje:	RU
Publicado:	Sankt-Peterburg : NIIÈM imeni Pastera 2019
Materias:	vitamin d deficiency, decision tree, machine learning,vitamin d, hepatitis b virus, roc curve Infectious and parasitic diseases RC109-216
Acceso en línea:	https://doaj.org/article/311a831fc1ba42e28056fabda885f5c5
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spelling	oai:doaj.org-article:311a831fc1ba42e28056fabda885f5c52021-11-22T07:09:56ZA Predictive Performance Analysis of Vitamin D Deficiency Using a Decision Tree model2220-76192313-739810.15789/2220-7619-AOT-1642https://doaj.org/article/311a831fc1ba42e28056fabda885f5c52019-06-01T00:00:00Zhttps://www.iimmun.ru/iimm/article/view/1642https://doaj.org/toc/2220-7619https://doaj.org/toc/2313-7398Background: VD is involved in various pathophysiological mechanisms in a plethora of diseases. And also, there is a strong demand for the prediction of its severity using different methods. The study aims to evaluate performance of DT as one of the machine learning models in the prediction of severity in VDD. Methods: In total, data containing serum Vitamin D levels were collected from 292 CHB patients. The independent characteristics such as: age, sex, weight, height, zinc, BMI, body fat, sunlight exposure, and milk consumption were used for prediction of VDD. 60% of them were allocated to a training dataset randomly. To evaluate the performance of decision-tree the remaining 40% were used as the testing dataset. The validation of the model was evaluated by ROC curve.Results: The prevalence of vitaminD3 deficiency was high among patients (63.0%). The final experimentation results showed that DT Classifier achieves better accuracy of 96 % and outperforms well on training and testing Vitamin D dataset. Conclusion: We concluded that the serum level of Zn is an important associated risk factor for identifying cases with vitamin D deficiency. Also, the risk of VDD could be predicted with high accuracy using decision tree learning algorithm that could be used for antiviral therapy in CHB patients.F. OsmaniSankt-Peterburg : NIIÈM imeni Pasteraarticlevitamin d deficiency, decision tree, machine learning,vitamin d, hepatitis b virus, roc curveInfectious and parasitic diseasesRC109-216RUInfekciâ i Immunitet, Vol 0, Iss 0 (2019)
institution	DOAJ
collection	DOAJ
language	RU
topic	vitamin d deficiency, decision tree, machine learning,vitamin d, hepatitis b virus, roc curve Infectious and parasitic diseases RC109-216
spellingShingle	vitamin d deficiency, decision tree, machine learning,vitamin d, hepatitis b virus, roc curve Infectious and parasitic diseases RC109-216 F. Osmani A Predictive Performance Analysis of Vitamin D Deficiency Using a Decision Tree model
description	Background: VD is involved in various pathophysiological mechanisms in a plethora of diseases. And also, there is a strong demand for the prediction of its severity using different methods. The study aims to evaluate performance of DT as one of the machine learning models in the prediction of severity in VDD. Methods: In total, data containing serum Vitamin D levels were collected from 292 CHB patients. The independent characteristics such as: age, sex, weight, height, zinc, BMI, body fat, sunlight exposure, and milk consumption were used for prediction of VDD. 60% of them were allocated to a training dataset randomly. To evaluate the performance of decision-tree the remaining 40% were used as the testing dataset. The validation of the model was evaluated by ROC curve.Results: The prevalence of vitaminD3 deficiency was high among patients (63.0%). The final experimentation results showed that DT Classifier achieves better accuracy of 96 % and outperforms well on training and testing Vitamin D dataset. Conclusion: We concluded that the serum level of Zn is an important associated risk factor for identifying cases with vitamin D deficiency. Also, the risk of VDD could be predicted with high accuracy using decision tree learning algorithm that could be used for antiviral therapy in CHB patients.
format	article
author	F. Osmani
author_facet	F. Osmani
author_sort	F. Osmani
title	A Predictive Performance Analysis of Vitamin D Deficiency Using a Decision Tree model
title_short	A Predictive Performance Analysis of Vitamin D Deficiency Using a Decision Tree model
title_full	A Predictive Performance Analysis of Vitamin D Deficiency Using a Decision Tree model
title_fullStr	A Predictive Performance Analysis of Vitamin D Deficiency Using a Decision Tree model
title_full_unstemmed	A Predictive Performance Analysis of Vitamin D Deficiency Using a Decision Tree model
title_sort	predictive performance analysis of vitamin d deficiency using a decision tree model
publisher	Sankt-Peterburg : NIIÈM imeni Pastera
publishDate	2019
url	https://doaj.org/article/311a831fc1ba42e28056fabda885f5c5
work_keys_str_mv	AT fosmani apredictiveperformanceanalysisofvitaminddeficiencyusingadecisiontreemodel AT fosmani predictiveperformanceanalysisofvitaminddeficiencyusingadecisiontreemodel
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A Predictive Performance Analysis of Vitamin D Deficiency Using a Decision Tree model

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