## 4 Discussion  
To help to bridge the gap between sustainability and project management, a methodology based on the SDGs was developed to assess sustainability in a sample of multi-sectoral  
Figure 8: Comparison of the overall accuracy metric for different classifiers during the training phase of the balanced model. Note: The orange horizontal line refers to the median and the green triangle refers to the mean. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Classifier** & **Class** & **tp** & **tn** & **fp** & **fn** & **Accuracy** & **Overall Accuracy** & **Precision** & **Recall** &
\begin{tabular}{c} **F1** \\ **Score** \\ \end{tabular} & **ROC/AUC** \\ \hline \multirow{3}{*}{LR} & High & 7 & 29 & 2 & 0 & 0.95 & & 0.78 & 1.00 & 0.88 & 0.98 \\  & Low & 6 & 30 & 0 & 2 & 0.95 & 0.89 & 1.00 & 0.75 & 0.86 & 1.00 \\  & Medium & 21 & 13 & 2 & 2 & 0.89 & & 0.91 & 0.91 & 0.91 & 0.97 \\ \hline \multirow{3}{*}{SVM} & High & 7 & 29 & 2 & 0 & 0.95 & & 0.78 & 1.00 & 0.88 & 0.99 \\  & Low & 7 & 30 & 0 & 1 & 0.97 & 0.92 & 1.00 & 0.88 & 0.93 & 1.00 \\  & Medium & 21 & 14 & 1 & 2 & 0.92 & & 0.95 & 0.91 & 0.93 & 0.98 \\ \hline \multirow{3}{*}{GAUSS} & High & 5 & 30 & 1 & 2 & 0.92 & & 0.83 & 0.71 & 0.77 & 0.98 \\  & Low & 6 & 30 & 0 & 2 & 0.95 & 0.87 & 1.00 & 0.75 & 0.86 & 1.00 \\  & Medium & 22 & 11 & 4 & 1 & 0.87 & & 0.85 & 0.96 & 0.90 & 0.97 \\ \hline \multirow{3}{*}{KNN} & High & 7 & 29 & 2 & 0 & 0.95 & & 0.78 & 1.00 & 0.88 & 0.98 \\  & Low & 6 & 29 & 1 & 2 & 0.92 & 0.87 & 0.86 & 0.75 & 0.80 & 0.99 \\ \cline{1-1}  & Medium & 20 & 13 & 2 & 3 & 0.87 & & 0.91 & 0.87 & 0.89 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Set of metrics of the balanced models (testing phase). projects in the Latin American and Caribbean region. This is important because, while there are numerous models that relate project management to sustainability in specific sectors, there are few that involve the SDGs in the creation of shared value. In this sense, [27] highlight the role of project managers in taking a leading role in the creation of a conceptual framework to measure the impacts of the SDGs on projects through the lens of TBL. However, the acquisition of competencies and tools is not always the most appropriate, meaning that solutions to problems are sought based on the immediacy and benefit of the results, instead of developing strategies that involve the entire project development process. Along the same lines, [65] and [66] consider that the SDGs should be deemed as key inputs to the business strategy and not as an additional external cost to the company, integrating them into the project life cycle. On the opposite side, other authors question the ability of the SDGs to determine project success. For example, [67] base their analysis on urban indicators and justify their argument by the variability and complexity of their definition; the political nature of the data; the scarce availability of standardized, open, and comparable data; the lack of solid institutions for data collection and monitoring; and, lastly, the difficulty of their application toward different local communities. Along the same lines, [68] provide a more environmentalist view and justify their argument in the proliferation of undefined and unmeasurable ideals, with the approach ambiguity of the SDGs 14 and 15 as an example. Artificial intelligence was used in this research article to diagnose, classify, and predict the sustainability of a sample of projects aimed at local communities in Latin America and the Caribbean, using the fulfillment of the SDGs as a reference. In the literature, we found experiences of classification and the classification of projects in relation to their degree of complexity [69], strategies [70], construction and sustainable infrastructure [71], and leadership styles [72], among others; however, we found no precedents for the classification of projects based on machine learning that took into account the level of compliance with the SDGs in local communities in Latin America and the Caribbean.