Classification-Based Screening of Depressive Disorder Patients Through Graph, Handwriting and Voice Signals

Laganaro, Francesca; Mazza, Marianna; Marano, Giuseppe; Piuzzi, Emanuele; Pallotti, Antonio

doi:10.1109/bats59463.2023.10303104

2023 International Workshop on Biomedical Applications, Technologies and Sensors (BATS) 2023

DOI: 10.1109/bats59463.2023.10303104

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Classification-Based Screening of Depressive Disorder Patients Through Graph, Handwriting and Voice Signals

Francesca Laganaro,

Marianna Mazza,

Giuseppe Marano

et al.

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2024

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Cited by 1 publication

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Machine Learning Models and Applications for Early Detection

Zapata-Cortes,

Arango-Serna,

Zapata-Cortes

et al. 2024

Sensors

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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.

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Machine Learning Models and Applications for Early Detection

Zapata-Cortes,

Arango-Serna,

Zapata-Cortes

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

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Classification-Based Screening of Depressive Disorder Patients Through Graph, Handwriting and Voice Signals

Cited by 1 publication

References 8 publications

Machine Learning Models and Applications for Early Detection

Machine Learning Models and Applications for Early Detection

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