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Review

Machine Learning Models and Applications for Early Detection

by
Orlando Zapata-Cortes
1,
Martin Darío Arango-Serna
2,
Julian Andres Zapata-Cortes
3,* and
Jaime Alonso Restrepo-Carmona
2
1
Instituto Tecnológico Metropolitano, Medellín 050034, Colombia
2
Facultad de Minas, Universidad Nacional de Colombia, Medellín 050034, Colombia
3
Fundación Universitaria CEIPA, Sabaneta 055450, Colombia
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(14), 4678; https://doi.org/10.3390/s24144678
Submission received: 17 June 2024 / Revised: 14 July 2024 / Accepted: 17 July 2024 / Published: 18 July 2024
(This article belongs to the Special Issue AI-Assisted Condition Monitoring and Fault Diagnosis)

Abstract

From the various perspectives of machine learning (ML) and the multiple models used in this discipline, there is an approach aimed at training models for the early detection (ED) of anomalies. The early detection of anomalies is crucial in multiple areas of knowledge since identifying and classifying them allows for early decision making and provides a better response to mitigate the negative effects caused by late detection in any system. This article presents a literature review to examine which machine learning models (MLMs) operate with a focus on ED in a multidisciplinary manner and, specifically, how these models work in the field of fraud detection. A variety of models were found, including Logistic Regression (LR), Support Vector Machines (SVMs), decision trees (DTs), Random Forests (RFs), naive Bayesian classifier (NB), K-Nearest Neighbors (KNNs), artificial neural networks (ANNs), and Extreme Gradient Boosting (XGB), among others. It was identified that MLMs operate as isolated models, categorized in this article as Single Base Models (SBMs) and Stacking Ensemble Models (SEMs). It was identified that MLMs for ED in multiple areas under SBMs’ and SEMs’ implementation achieved accuracies greater than 80% and 90%, respectively. In fraud detection, accuracies greater than 90% were reported by the authors. The article concludes that MLMs for ED in multiple applications, including fraud, offer a viable way to identify and classify anomalies robustly, with a high degree of accuracy and precision. MLMs for ED in fraud are useful as they can quickly process large amounts of data to detect and classify suspicious transactions or activities, helping to prevent financial losses.
Keywords: machine learning models; early detection; data analysis; fraud detection; performance metrics machine learning models; early detection; data analysis; fraud detection; performance metrics

Share and Cite

MDPI and ACS Style

Zapata-Cortes, O.; Arango-Serna, M.D.; Zapata-Cortes, J.A.; Restrepo-Carmona, J.A. Machine Learning Models and Applications for Early Detection. Sensors 2024, 24, 4678. https://doi.org/10.3390/s24144678

AMA Style

Zapata-Cortes O, Arango-Serna MD, Zapata-Cortes JA, Restrepo-Carmona JA. Machine Learning Models and Applications for Early Detection. Sensors. 2024; 24(14):4678. https://doi.org/10.3390/s24144678

Chicago/Turabian Style

Zapata-Cortes, Orlando, Martin Darío Arango-Serna, Julian Andres Zapata-Cortes, and Jaime Alonso Restrepo-Carmona. 2024. "Machine Learning Models and Applications for Early Detection" Sensors 24, no. 14: 4678. https://doi.org/10.3390/s24144678

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