# VITA-D: A Radionic Web Tool for Predicting Vitamin D Deficiency Levels
## 2 Related Work  
The literature revealed [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40] that a significantly higher proportion of patients may suffer from vitamin D deficiency compared to what is really reflected, suggesting that many cases go undiagnosed and untreated. Machine learning (ML) models have acquired great importance in the health sector for disease prediction. Recent studies have proved that applying ML models can automatically extract patterns from the available data and yield better results [29]. [PERSON], [PERSON] [41] trained different ML and DL algorithms employing 11 datasets in different biological, biomedical, and nature categories such as molecular interactions, cancer, clinical diagnosis, behavior related predictions, RGB images of human skin, and X-ray images of COVID-19 and cardiomegaly patients, demonstrating that the performance of the training phases improved with hyperparameter tuning tools. [PERSON] et al. [42] implemented clinical NLP (Natural Language Program) models like Bidirectional Encoder Representations from Transformers (BERT) to analyze patient records for improved risk stratification and to extract meaningful insights from unstructured clinical notes, aiding in decision making and patient management. Likewise, models such as Support Vector Machines (SVMs) and ensemble methods predict the effectiveness of treatments based on genetic and phenotypic data. This enables tailoring treatments to individual patients. The Cancer Genome Atlas (TCGA) project has employed ML to stratify cancer types and predict survival rates based on genomic profiles, as reported by [PERSON] et al. [43]. Also, ML models help in identifying drug candidates, predicting drug-target interactions, and optimizing clinical trials. Generative models (GANs) like variational autoencoders (VAEs) and reinforcement learning algorithms have accelerated drug discovery. [PERSON] et al. [44] used deep learning to discover new antibiotics, identifying Halicin, a potent antibiotic against drug-resistant bacteria. ML models like Random Forests and Gradient Boosting Machines are used to predict patient outcomes, readmission risks, and complications based on electronic health records (EHRs). These models support proactive interventions. [PERSON] et al. [45] applied deep learning to EHR data to predict hospital mortality and discharge outcomes. ML models, particularly Convolutional Neural Networks (CNNs), have been applied in interpretingmedical images such as X-rays, MRIs, and CT scans. They assist in identifying abnormalities like tumors, fractures, and infections with high accuracy. For example, [PERSON] et al. [46] demonstrated that a deep learning algorithm could achieve dermatologist-level accuracy in diagnosing skin cancer using thermoscopic images. [PERSON] et al. [30] explored the effectiveness of various ML models in predicting deficiency in vitamin D in 3044 students; independent parameters such as age, weight, and sun exposure were analyzed. The authors applied multiple classifiers, including K-Nearest Neighbor and Random Forest, with the latter standing out for its high accuracy of 96% on test sets. The approach of this research suggests that ML models can be a valuable tool for the prediction of vitamin D deficiency. [PERSON] et al. [31] evaluated the relationship between vitamin D levels and clinical histories in patients with acute ischemic stroke. Using data from 328 patients, the researchers applied ML algorithms such as Logistic Regression and Extreme Gradient Boost (XGBoost) to analyze the data. The results indicated that vitamin D deficiency was associated with higher rates of adverse outcomes. Specifically, the application of logistic regression showed an Area Under the Curve (AUC) of 0.746. [PERSON] et al. [32] studied the impact of vitamin D insufficiency in patients with systemic lupus erythematosus in southern Bangladesh.