A Predictive Performance Analysis of Vitamin D Deficiency Severity Using Machine Learning Methods
Loading...
Date
2020
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE Access
Abstract
Vitamin D Deficiency (VDD) is one of the most significant global health problem and there is
a strong demand for the prediction of its severity using non-invasive methods. The primary data containing
serum Vitamin D levels were collected from a total of 3044 college students between 18-21 years of age.
The independent parameters like age, sex, weight, height, body mass index (BMI), waist circumference,
body fat, bone mass, exercise, sunlight exposure, and milk consumption were used for prediction of VDD.
The study aims to compare and evaluate different machine learning models in the prediction of severity
in VDD. The objectives of our approach are to apply various powerful machine learning algorithms in
prediction and evaluate the results with different performance measures like Precision, Recall, F1-measure,
Accuracy, and Area under the curve of receiver operating characteristic (ROC). The McNemar’s test was
conducted to validate the empirical results which is a statistical test. The final objective is to identify the best
machine learning classifier in the prediction of the severity of VDD. The most popular and powerful machine
learning classifiers like K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), AdaBoost
(AB), Bagging Classifier (BC), ExtraTrees (ET), Stochastic Gradient Descent (SGD), Gradient Boosting
(GB), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) were implemented to predict the
severity of VDD. The final experimentation results showed that the Random Forest Classifier achieves better
accuracy of 96 % and outperforms well on training and testing Vitamin D dataset. The McNemar’s statistical
test results support that the RF classifier outperforms than the other classifiers.
Description
Keywords
Machine learning algorithms, Random forest classifier, severity of VDD, Vitamin D
Citation
Sambasivam, G., Amudhavel, J., & Sathya, G. (2020). A predictive performance analysis of vitamin D deficiency severity using machine learning methods. IEEE Access, 8, 109492-109507.