Performance of Statistical Models to Predict Vitamin D Levels

Bechrouri, Souad; Monir, Abdelilah; Mraoui, H.; Sebbar, E.; Saalaoui, Ennouamane; Choukri, Mohammed

doi:10.1145/3314074.3314076

Cited by 6 publications

(6 citation statements)

References 14 publications

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“…The multivariate and time series analysis was used for the detection of vitamin D levels in patients [17]. The biochemical parameters such as age, calcium, chlorine, LDLcholesterol, HDL-cholesterol [8], BMI, and WC [18] in the prediction of VDD using statistical models like Random forests, Support vector regression, linear regression, and Multivariable Adaptive Regression Spline. They have used error measures like Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE) for comparison and there is a weak correlation between the vitamin D and biochemical parameters [8].…”

Section: Related Workmentioning

confidence: 99%

“…Too much oversampling results in overfitting problem, so that we have not applied SMOTE to the test set. The prediction model was developed by using multivariate logistic regression [8]. To estimate the best prediction model, statistical test like McNemar's test were conducted [21].…”

Section: Related Workmentioning

confidence: 99%

“…19. a) Mathews' Correlation Coefficient (MCC): The Mathew correlation coefficient will be used as a measure of predicting the quality of a multiclass classification [8], [43].…”

Section: Correlation and Error Measuresmentioning

confidence: 99%

“…The MCC values of RF models has obtained 0.03 which has the highest HL between the predicted and target and GNB models has 0.54 the worst HL values. c) MAE: It signifies the distinction between the trained dataset and the predicted values of the trained dataset that is separated from the mean difference over the dataset [8]. It can also be called mean of absolute error.…”

Section: B) Hamming Lossmentioning

confidence: 99%

“…The traditional severity prediction of VDD have used questionnaires [7] with statistical models such as Linear Regression (LR), Multivariable Adaptive regression Spline, Support Vector Regression (SVR) and support vector regression classifiers [8] to predict the severity of VDD. In previous studies, the results were compared between the statistical models and they have not used the machine learning algorithms for the severity prediction.…”

Section: Introductionmentioning

confidence: 99%

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A Predictive Performance Analysis of Vitamin D Deficiency Severity Using Machine Learning Methods

2020

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…19. a) Mathews' Correlation Coefficient (MCC): The Mathew correlation coefficient will be used as a measure of predicting the quality of a multiclass classification [8], [43].…”

Section: Correlation and Error Measuresmentioning

confidence: 99%

Section: B) Hamming Lossmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Predictive Performance Analysis of Vitamin D Deficiency Severity Using Machine Learning Methods

2020

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Contribution of artificial intelligence and big data in a medical biology laboratory: An experience of the central laboratory CHU Mohammed VI Oujda

Yahyaoui

Zrouri

Hamdani

et al. 2023

Materials Today: Proceedings

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Forecasting levels of serum 25-hydroxyvitamin D based on dietary intake, lifestyle and personal determinants in a sample of Southern Europeans

et al. 2023

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Vitamin D is an essential nutrient to be consumed in the habitual dietary intake, whose deficiency is associated with various disturbances. This study represents a validation of vitamin D status estimation using a semi-quantitative Food Frequency Questionnaire (FFQ), together with data from additional physical activity and lifestyle questionnaires. This information was combined to forecast the serum vitamin D status. Different statistical methods were applied to estimate the vitamin D status using predictors based on diet and lifestyle. Serum vitamin D was predicted using linear regression (with leave-one-out cross-validation) and Random Forest models. Intraclass correlation coefficients, Lin’s agreement coefficients, Bland-Altman plots, and other methods were used to assess the accuracy of the predicted versus observed serum values. Data were collected in Spain. A total of 220 healthy volunteers aged between 18 and 78 years were included in this study. They completed validated questionnaires and agreed to provide blood samples to measure serum 25(OH)D levels. The common final predictors in both models were age, sex, sunlight exposure, vitamin D dietary intake (as assessed by the FFQ), body mass index, time spent walking, physical activity, and skin reaction after sun exposure. The intraclass correlation coefficient for the prediction was 0.60 (95% CI: 0.52-0.67; p<0.001) using the Random Forest model. The magnitude of the correlation was moderate, which means that our estimation could be useful in future epidemiological studies to establish a link between the predicted 25(OH)D values and the occurrence of several clinical outcomes in larger cohorts.

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Performance of Statistical Models to Predict Vitamin D Levels

Cited by 6 publications

References 14 publications

A Predictive Performance Analysis of Vitamin D Deficiency Severity Using Machine Learning Methods

A Predictive Performance Analysis of Vitamin D Deficiency Severity Using Machine Learning Methods

Contribution of artificial intelligence and big data in a medical biology laboratory: An experience of the central laboratory CHU Mohammed VI Oujda

Forecasting levels of serum 25-hydroxyvitamin D based on dietary intake, lifestyle and personal determinants in a sample of Southern Europeans

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