Factors determining the serum 25‐hydroxyvitamin D response to vitamin D supplementation: Data mining approach

Abstract

Vitamin D supplementation has been shown to prevent vitamin D deficiency, but various factors can affect the response to supplementation. Data mining is a statistical method for pulling out information from large databases. We aimed to evaluate the factors influencing serum 25-hydroxyvitamin D levels in response to supplementation of vitamin D using a random forest (RF) model. Data were extracted from the survey of ultraviolet intake by nutritional approach study. Vitamin D levels were measured at baseline and at the end of study to evaluate the responsiveness. We examined the relationship between 76 potential influencing factors on vitamin D response using RF. We found several features that were highly correlated to the serum vitamin D response to supplementation by RF including anthropometric factors (body mass index [BMI], free fat mass [FFM], fat percentage, waist-to-hip ratio [WHR]), liver function tests (serum gamma-glutamyl transferase [GGT], total bilirubin, total protein), hematological parameters (mean corpuscular volume [MCV], mean corpuscular hemoglobin concentration [MCHC], hematocrit), and measurement of insulin sensitivity (homeostatic model assessment of insulin resistance). BMI, total bilirubin, FFM, and GGT were found to have a positive relationship and homeostatic model assessment for insulin resistance, MCV, MCHC, fat percentage, total protein, and WHR were found to have a negative correlation to vitamin D concentration in response to supplementation. The accuracy of RF in predicting the response was 93% compared to logistic

Research on User Demand Determination Method and Marketing Strategy for Home Broadband Business Based on Data Mining

“…In equation ( 4), _ PM PLAntennaGain represents the antenna gain in the distance from the base station to the grid point, and OtherLoss represents other losses in the distance. Based on this, the information expression of grid fingerprints can also be obtained as equation (5). In equation ( 5), BSPWR for the transmission power of the target area, subtracting the transmission power from the total path loss can obtain the field strength information in the grid points, which is the grid fingerprint information.…”

“…This study evaluated 76 factors including body mass index and total bilirubin, and validated the correlation. Finally, the evaluation accuracy of random forest model is 93% [5]. In order to solve the environmental pollution problem in blast furnace ironmaking, Kou et al established six evaluation systems, processed them using Python, identified potential factors, and then established a comprehensive evaluation model through factor analysis.…”

Applying data mining techniques to predict vitamin D deficiency in diabetic patients

“…They achieved an accuracy rate of 93% using RF to determine the factors affecting the response of vitamin D supplementation. 30 Padmajaa et al (2021) used machine learning algorithms including RF, multi-layer perceptron, k-nearest neighbor, SVM, decision tree, gradient boosting, stochastic gradient descent, adaboost classifier, extra trees classifier algorithm, and LR. In their study in which the prognosis of vitamin D deficiency severity was estimated and extra trees classifier algorithm achieved an accuracy rate of 73.3%.…”