TY - JOUR
T1 - Selecting the best machine learning algorithm to support the diagnosis of Non-Alcoholic Fatty Liver Disease
T2 - A meta learner study
AU - MICOL Group
AU - Sorino, Paolo
AU - Caruso, Maria Gabriella
AU - Misciagna, Giovanni
AU - Bonfiglio, Caterina
AU - Campanella, Angelo
AU - Mirizzi, Antonella
AU - Franco, Isabella
AU - Bianco, Antonella
AU - Buongiorno, Claudia
AU - Liuzzi, Rosalba
AU - Cisternino, Anna Maria
AU - Notarnicola, Maria
AU - Chiloiro, Marisa
AU - Pascoschi, Giovanni
AU - Osella, Alberto Ruben
N1 - Funding Information:
This work was funded by a grant from the Ministry of Health, Italy (Progetto Finalizzato del Ministero della Salute, ICS 160.2/RF 2003), 2004/ 2006) and by Apulia Region-D.G.R. n. 1159, 28/6/ 2018 and 2019.
Publisher Copyright:
© 2020 Sorino et al.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/10
Y1 - 2020/10
N2 - Background & aims Liver ultrasound scan (US) use in diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD) causes costs and waiting lists overloads. We aimed to compare various Machine learning algorithms with a Meta learner approach to find the best of these as a predictor of NAFLD. Methods The study included 2970 subjects, 2920 constituting the training set and 50, randomly selected, used in the test phase, performing cross-validation. The best predictors were combined to create three models: 1) FLI plus GLUCOSE plus SEX plus AGE, 2) AVI plus GLUCOSE plus GGT plus SEX plus AGE, 3) BRI plus GLUCOSE plus GGT plus SEX plus AGE. Eight machine learning algorithms were trained with the predictors of each of the three models created. For these algorithms, the percent accuracy, variance and percent weight were compared. Results The SVM algorithm performed better with all models. Model 1 had 68% accuracy, with 1% variance and an algorithm weight of 27.35; Model 2 had 68% accuracy, with 1% variance and an algorithm weight of 33.62 and Model 3 had 77% accuracy, with 1% variance and an algorithm weight of 34.70. Model 2 was the most performing, composed of AVI plus GLUCOSE plus GGT plus SEX plus AGE, despite a lower percentage of accuracy. Conclusion A Machine Learning approach can support NAFLD diagnosis and reduce health costs. The SVM algorithm is easy to apply and the necessary parameters are easily retrieved in databases.
AB - Background & aims Liver ultrasound scan (US) use in diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD) causes costs and waiting lists overloads. We aimed to compare various Machine learning algorithms with a Meta learner approach to find the best of these as a predictor of NAFLD. Methods The study included 2970 subjects, 2920 constituting the training set and 50, randomly selected, used in the test phase, performing cross-validation. The best predictors were combined to create three models: 1) FLI plus GLUCOSE plus SEX plus AGE, 2) AVI plus GLUCOSE plus GGT plus SEX plus AGE, 3) BRI plus GLUCOSE plus GGT plus SEX plus AGE. Eight machine learning algorithms were trained with the predictors of each of the three models created. For these algorithms, the percent accuracy, variance and percent weight were compared. Results The SVM algorithm performed better with all models. Model 1 had 68% accuracy, with 1% variance and an algorithm weight of 27.35; Model 2 had 68% accuracy, with 1% variance and an algorithm weight of 33.62 and Model 3 had 77% accuracy, with 1% variance and an algorithm weight of 34.70. Model 2 was the most performing, composed of AVI plus GLUCOSE plus GGT plus SEX plus AGE, despite a lower percentage of accuracy. Conclusion A Machine Learning approach can support NAFLD diagnosis and reduce health costs. The SVM algorithm is easy to apply and the necessary parameters are easily retrieved in databases.
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U2 - 10.1371/journal.pone.0240867
DO - 10.1371/journal.pone.0240867
M3 - Article
C2 - 33079971
AN - SCOPUS:85093834677
VL - 15
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 10 October
M1 - e0240867
ER -