TY  - JOUR
KW  - SMOTE; artificial intelligence; projects; fuzzy logic
N2  - The purpose of this article is to help to bridge the gap between sustainability and its application to project management by developing a methodology based on artificial intelligence to diagnose, classify, and forecast the level of sustainability of a sample of 186 projects aimed at local communities in Latin American and Caribbean countries. First, the compliance evaluation with the Sustainable Development Goals (SDGs) within the framework of the 2030 Agenda served to diagnose and determine, through fuzzy sets, a global sustainability index for the sample, resulting in a value of 0.638, in accordance with the overall average for the region. Probabilistic predictions were then made on the sustainability of the projects using a series of supervised learning classifiers (SVM, Random Forest, AdaBoost, KNN, etc.), with the SMOTE resampling technique, which provided a significant improvement toward the results of the different metrics of the base models. In this context, the Support Vector Machine (SVM) + SMOTE was the best classification algorithm, with accuracy of 0.92. Lastly, the extrapolation of this methodology is to be expected toward other realities and local circumstances, contributing to the fulfillment of the SDGs and the development of individual and collective capacities through the management and direction of projects.
UR  - http://doi.org/10.3390/app122111188
VL  - 12
ID  - unic4474
A1  - García Villena, Eduardo
A1  - Pascual Barrera, Alina Eugenia
A1  - Álvarez, Roberto Marcelo
A1  - Dzul López, Luis Alonso
A1  - Tutusaus, Kilian
A1  - Vidal Mazón, Juan Luis
A1  - Miró Vera, Yini Airet
A1  - Brie, Santiago
A1  - López Flores, Miguel A.
IS  - 21
JF  - Applied Sciences
TI  - Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean
SN  - 2076-3417
AV  - public
Y1  - 2022/11//
ER  -