Bechrouri, S. and Monir, A. and Mraoui, H. and Sebbar, E.H. and Saalaoui, E. and Choukri, M. (2019) Performance of statistical models to predict Vitamin D levels. In: UNSPECIFIED.

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Abstract

Strong demand for vitamin D diagnosis was observed in recent years. This trend has ledto an increase in public health expenditures as well as for the patient. The vitamin D prediction was an alternative for saving patients the extra charge of blood tests. We compared some statistical methods in order to predict the vitamin D levels based solely on biochemical parameters, age, and sex. A set of hospitalized patients from different departments of the University Hospital Centre of Oujda and having a valid value for vitamin D and various biochemical parameters were included. There were 124 patients aged between 9 to 87 years old (mean = 45.19, median = 49) and 17 variables. Vitamin D was predicted using linear regression, Multivariable Adaptive Regression Spline (MARS), Random Forest (RF) and Support Vector Regression (SVR). Two statistics were used to compare the different models: Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE). The test correlation wasshowed weak correlations between vitamin D and some biochemical parameters such as calcium and glucose. These comparisons were demonstrated the SVR model performedbetter thanrandom forests and MARS in the case of a small size database. This prediction may help to identify patients with a real risk of vitamin D deficiency. © 2019 Association for Computing Machinery.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Bioinformatics; Classification (of information); Decision trees; Diagnosis; Error statistics; Forecasting; Hospitals; Mean square error; Medical informatics; Regression analysis, Applied computing; Biochemical parameters; Health expenditures; Mean absolute error; Regression splines; Root mean squared errors; Support vector regression (SVR); Weak correlation, Vitamins
Subjects: Computer Science
Divisions: SCIENTIFIC PRODUCTION > Computer Science
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:45
URI: http://eprints.umi.ac.ma/id/eprint/2218

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