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This study applies supervised machine learning to classify astrocytes from a small sample into distinct morphological categories using a dataset of 193 cells with unbalanced classes and missing observations. Classification trees and ensemble methods, including boosting and bagging with under sampling, assess nuclear morphology of LMNB1-stained astrocytes. 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Predicting Astrocytic Nuclear Morphology with Machine Learning: A Tree Ensemble Classifier Study. Appl. Sci. 2023, 13, 4289 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)app13074289  \nAcademic Editors: Ana Cristina Braga and Juan A. Gâmez-Pulido  \nReceived: 3 January 2023  \nRevised: 23 February 2023  \nAccepted: 22 March 2023  \nPublished: 28 March 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Public Health and Pediatrics, University of Torino, Via Santena 5 bis, 10126 Torino, Italy  \n2 Department of Neuroscience Rita Levi-Montalcini, University of Torino, Via Cherasco 15, 10126 Torino, Italy  \n3 Neuroscience Institute Cavalieri Ottolenghi (NICO), University of Torino, Regione Gonzole 43,  \n10043 Orbassano, Italy  \n4 Center for Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Torino, Regione Gonzole 43, 10043 Orbassano, Italy  \n* Correspondence: paola.berchialla@unito.it; Tel.: +39-011-670-5813 † Both authors should be considered co-first authors.  \n‡ Both authors should be considered last author.  \nAbstract: Machine learning is usually associated with big data; however, experimental or clinical data are usually limited in size. The aim of this study was to describe how supervised machine learning can be used to classify astrocytes from a small sample into different morphological classes. Our dataset was composed of only 193 cells, with unbalanced morphological classes and missing observations. We combined classification trees and ensemble algorithms (boosting and bagging) with under sampling to classify the nuclear morphology (homogeneous, dotted, wrinkled, forming crumples, and forming micronuclei) of astrocytes stained with anti-LMNB1 antibody. Accuracy, sensitivity (recall), specificity, and F1 score were assessed with bootstrapping, leave one-out (LOOCV) and stratified cross-validation. We found that our algorithm performed at rates above chance in predicting the morphological classes of astrocytes based on the nuclear expression of LMNB1 . Boosting algorithms (tree ensemble) yielded better classifications over bagging ones (tree bagger) . Moreover leave-one-out and bootstrapping yielded better predictions than the more commonly used k-fold cross-validation. Finally, we could identify four important predictors: the intensity of LMNB1 expression, nuclear area, cellular area, and soma area. Our results show that a tree ensemble can be optimized, in order to classify morphological data from a small sample, even in the presence of highly unbalanced classes and numerous missing data.  \nKeywords: machine learning; astrocytes; LMNB1; tree ensemble; cross-validation; tree bagger; classification tree  \n1. Introduction  \n1.1. Background  \nMachine learning (ML) has seen a significant advancement in recent years. New techniques, such as deep learning, are becoming more prevalent in tools we use in everyday life. ML is now being used in fields that were previously thought impossible, including vehicle detection and traffic density prediction [1], smart agriculture with deep convolutional neural networks [1–4], and even in the medical field for diagnoses and monitoring [5–8] . Deep learning can also detect fake reviews [9] and leaf disease in agriculture [10] . These","cbCaicdaSfqlveaN","https://ap.wps.com/l/cbCaicdaSfqlveaN","pdf",3004670,14,"English","# Abstract\n# Introduction\n## Background\n## Related Works","[{\"question\":\"What was the main goal of the study?\",\"answer\":\"To use supervised machine learning to classify astrocytes from a small sample into different morphological classes.\"},{\"question\":\"How was the dataset constructed and what challenges were present?\",\"answer\":\"The dataset contained 193 cells, with unbalanced morphological classes and missing observations.\"},{\"question\":\"Which evaluation methods and predictors were used?\",\"answer\":\"Accuracy, sensitivity, specificity, and F1 score were assessed using bootstrapping, LOOCV, and stratified cross-validation, and four predictors were identified: LMNB1 intensity, nuclear area, cellular area, and soma area.\"}]","Predicting Astrocytic Nuclear Morphology with Machine Learning - A Tree Ensemble Classifier Study | PDF",35]