# VITA-D: A Radionic Web Tool for Predicting Vitamin D Deficiency Levels
## 7 Limitations, Implications, and Future Works  
Despite the promising results, there are limitations to this study. One limitation is the relatively small sample size, which may not represent the broader population, and we faced limited access to public and private clinical databases in Latin America and worldwide. The study's sample size (465 private and 634 public patients) may limit its generalizability to broader populations. It is particularly skewed toward a specific geographic region (Loja, Ecuador), which might not represent other populations with differing lifestyles, genetics, and environmental factors. Additionally, the dataset sources used in the study may not capture all relevant variables that influence vitamin D levels, potentially limiting the model's generalizability. Another limitation is the focus on a binary classification of vitamin D levels (deficient/sufficient), which might oversimplify the complexity of vitamin D metabolism. This binary approach overlooks nuanced variations in vitamin D metabolism, potentially limiting the utility of the tool for detailed clinical assessments. Although 18 variables were selected based on clinical relevance, other critical factors such as genetic predispositions, dietary intake, or seasonal variations were excluded. These omissions might impact the model's predictive accuracy. The computational requirements for the tool's back-end systems, particularly with machine learning models, may limit its scalability for real-time predictions in high-demand scenarios. By addressing these limitations and building on the current practical strengths, the tool could evolve into a more robust and globally applicable solution for managing vitamin D deficiency. The main implication of the \"VITA-D\" tool is that it offers a cost-effective alternative to traditional vitamin D tests, particularly beneficial in resource-limited settings. Its predictive capabilities could aid in early detection and intervention, reducing the burden of diseases associated with vitamin D deficiency. By integrating sociodemographic and clinical data, the tool can help identify at-risk populations, enabling targeted public health campaigns and resource allocation. The web-based interface ensures broader accessibility, empowering non-specialist healthcare providers and individuals to assess vitamin D status conveniently. The tool's compatibility with electronic health records (EHRs) could enhance patient monitoring and personalized treatment plans. For future work, the research could be expanded by incorporating larger, more diverse datasets to explore a continuous prediction of vitamin D levels rather than binary classification, to improve the robustness and generalizability of the predictive model, and also including other variables related to lifestyle, genetic factors, and environmental influences could enhance prediction accuracy. Further refinement of the ML algorithms and the inclusion of additional deep learning models like Convolutional Neural Networks (CNNs) or Transformer-based models could lead to improved predictive accuracy. Performing model optimization through ensemble techniques and hyperparameter tuning using optimized computational efficiency would allow for deployment on low-resource devices or offline access. Moreover, extending the web tool's functionality to include more detailed, continuous predictions of vitamin D levels also presents a direction for improvement. Developing multilingual and region-specific versions of the web application could help to reach broader demographics. Ensuring open access to the tool for underrepresented and resource-limited populations would improve accessibility, and applying a greater number of clinical tests to the app would ensure its accuracy, reliability, and validity. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15041798/s1](https://www.mdpi.com/article/10.3390/app15041798/s1)]([https://www.mdpi.com/article/10.3390/app15041798/s1](https://www.mdpi.com/article/10.3390/app15041798/s1)).