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Volume 10
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Article

Predicting Outcome of Traumatic Brain Injury: Is Machine Learning the Best Way?

by
Roberta Bruschetta
1,2,
Gennaro Tartarisco
1,*,
Lucia Francesca Lucca
3,
Elio Leto
3,
Maria Ursino
3,
Paolo Tonin
3,
Giovanni Pioggia
1 and
Antonio Cerasa
1,3,4,*
1
Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy
2
Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
3
S’Anna Institute, 88900 Crotone, Italy
4
Pharmacotechnology Documentation and Transfer Unit, Preclinical and Translational Pharmacology, Department of Pharmacy, Health Science and Nutrition, University of Calabria, 87036 Arcavacata di Rende, Italy
*
Authors to whom correspondence should be addressed.
Biomedicines 2022, 10(3), 686; https://doi.org/10.3390/biomedicines10030686
Submission received: 10 February 2022 / Revised: 2 March 2022 / Accepted: 14 March 2022 / Published: 16 March 2022
(This article belongs to the Special Issue State of the Art: Neurodegenerative Diseases in Italy)
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Abstract

One of the main challenges in traumatic brain injury (TBI) patients is to achieve an early and definite prognosis. Despite the recent development of algorithms based on artificial intelligence for the identification of these prognostic factors relevant for clinical practice, the literature lacks a rigorous comparison among classical regression and machine learning (ML) models. This study aims at providing this comparison on a sample of TBI patients evaluated at baseline (T0), after 3 months from the event (T1), and at discharge (T2). A Classical Linear Regression Model (LM) was compared with independent performances of Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Naïve Bayes (NB) and Decision Tree (DT) algorithms, together with an ensemble ML approach. The accuracy was similar among LM and ML algorithms on the analyzed sample when two classes of outcome (Positive vs. Negative) approach was used, whereas the NB algorithm showed the worst performance. This study highlights the utility of comparing traditional regression modeling to ML, particularly when using a small number of reliable predictor variables after TBI. The dataset of clinical data used to train ML algorithms will be publicly available to other researchers for future comparisons.
Keywords: traumatic brain injury; outcome predictors; linear regression; machine learning; ensemble of classifiers traumatic brain injury; outcome predictors; linear regression; machine learning; ensemble of classifiers

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MDPI and ACS Style

Bruschetta, R.; Tartarisco, G.; Lucca, L.F.; Leto, E.; Ursino, M.; Tonin, P.; Pioggia, G.; Cerasa, A. Predicting Outcome of Traumatic Brain Injury: Is Machine Learning the Best Way? Biomedicines 2022, 10, 686. https://doi.org/10.3390/biomedicines10030686

AMA Style

Bruschetta R, Tartarisco G, Lucca LF, Leto E, Ursino M, Tonin P, Pioggia G, Cerasa A. Predicting Outcome of Traumatic Brain Injury: Is Machine Learning the Best Way? Biomedicines. 2022; 10(3):686. https://doi.org/10.3390/biomedicines10030686

Chicago/Turabian Style

Bruschetta, Roberta, Gennaro Tartarisco, Lucia Francesca Lucca, Elio Leto, Maria Ursino, Paolo Tonin, Giovanni Pioggia, and Antonio Cerasa. 2022. "Predicting Outcome of Traumatic Brain Injury: Is Machine Learning the Best Way?" Biomedicines 10, no. 3: 686. https://doi.org/10.3390/biomedicines10030686

APA Style

Bruschetta, R., Tartarisco, G., Lucca, L. F., Leto, E., Ursino, M., Tonin, P., Pioggia, G., & Cerasa, A. (2022). Predicting Outcome of Traumatic Brain Injury: Is Machine Learning the Best Way? Biomedicines, 10(3), 686. https://doi.org/10.3390/biomedicines10030686

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