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 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.