# VITA-D: A Radionic Web Tool for Predicting Vitamin D Deficiency Levels
## 2 Related Work
The study aimed to assess the risk factors associated with low vitamin D levels in these patients by collecting clinical and demographic data from 50 patients, using Random Forest and Linear Regression analysis. These models allowed the identification of significant variables such as hemoglobin and age, which predicted vitamin D levels. The Random Forest model, optimized by cross-validation, stood out for its ability to handle collinearity and interactions between variables, achieving a root mean square error (RMSE) of 2.98 and a mean absolute error (MAE) of 2.68. [PERSON] et al. [33] compared regression methods in predicting vitamin D deficiency in an obese hypertensive population. Using data on 221 patients, they implemented Logistic Regression and two ML methods (LASSO and ElasticNet). These algorithms allowed for the selection of relevant clinical and laboratory characteristics, highlighting the role of variables such as blood pressure and low-density lipoprotein (LDL) cholesterol levels in predicting vitamin D deficiency. The ML models showed better performance concerning standard logistic regression, evidenced by a lower classification error rate and a higher AUC (LASSO = 0.74 and Elastic Net =0.76, compared to logistic regression = 0.64). Also, ref. [34] examined how to predict vitamin D deficiency in hypertensive patients. The study collected data from 1002 patients from a Spanish university hospital, employing algorithms such as Logistic Regression and SVM to analyze the data. The models could accurately identify those patients at increased risk of vitamin D deficiency using a combination of feature selection methods and classification algorithms. The SVM method proved to be superior in sensitivity, achieving 98%. [PERSON] and [PERSON] et al. [35] addressed the detection of vitamin D levels in adults using ML algorithms. Data from 481 patients from a private hospital were used and analyzed using Ordinal Logistic Regression (Elastic-net), SVM, and Random Forest. The results showed that Random Forest outperformed the other two models with an accuracy of 94%, sensitivity of 94%, and an F1 score of 0.95, proving effective in small data samples. [PERSON] et al. [36] explored vitamin D deficiency using ML algorithms on 350 patients. The study compared several algorithms, including Logistic Regression, SVM, Random Forest, and OptRF. OptRF proved superior in accuracy (91.42%), compared to SVM (64%) and Logistic Regression (73%). [PERSON] et al. [37] investigated the correlation between vitamin D levels and neurological deficits in patients with cerebral infarction. The study used 200 patients to train Logistic Regression algorithms and XGBoost as predictive models. The authors selected vitamin D deficiency levels, cholesterol, and blood pressure variables. The XGBoost algorithm, adjusted by cross-validation (70-30% training and testing, respectively), showed superior performance compared to Logistic Regression, with an AUC of 0.786 in the test set, compared to 0.761 for Logistic Regression. [PERSON] et al. [38] addressed the prediction of vitamin D deficiency in older adults using ML models. The study used data from 5106 adults, randomly selecting 65% for training and the remaining 35% for testing. The developed models included Lasso Regression, Elastic Net, Random Forest, and Decision Tree, compared with a reference model based on Logistic Regression. The ML models showed similar accuracy in predicting 25-OH-D levels below 50 nmol/L. However, these models were superior in predicting levels below 25 nmol/L, with elastic regression achieving the highest AUC (0.93) compared to the reference model (0.81). [PERSON] et al. [5] conducted a study to explore the predictive ability of anthropometric parameters on vitamin D deficiency using ML methods with a population of 501 adults. Data were selected from random sampling with replacement, and 70% were used for training and 30% for validation. The study highlighted the effectiveness of Logistic Regression and Naive Bayes models with an accuracy of 64.1% and 65.3%, respectively.