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Computational prediction of diagnosis and feature selection on mesothelioma patient health records

PloS one, 2019-01, Vol.14 (1), p.e0208737-e0208737 [Peer Reviewed Journal]

COPYRIGHT 2019 Public Library of Science ;COPYRIGHT 2019 Public Library of Science ;2019 Chicco, Rovelli. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2019 Chicco, Rovelli 2019 Chicco, Rovelli ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0208737 ;PMID: 30629589

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Title:
Computational prediction of diagnosis and feature selection on mesothelioma patient health records
Author: Chicco, Davide ; Rovelli, Cristina
Vellido, Alfredo
Subjects: Accuracy ; Archives & records ; Artificial intelligence ; Asbestos ; Bioinformatics ; Biology and Life Sciences ; Biopsy ; Blood platelets ; Care and treatment ; Classification ; Classifiers ; Clinical decision making ; Computational Biology - methods ; Computer and Information Sciences ; Computer applications ; Correlation coefficient ; Correlation coefficients ; Datasets ; Decision making ; Decision trees ; Diagnosis ; Engineering and Technology ; Exposure ; Female ; Health Records, Personal ; Humans ; Imaging techniques ; Learning algorithms ; Lung cancer ; Lung diseases ; Machine Learning ; Male ; Medical diagnosis ; Medical imaging ; Medical personnel ; Medical prognosis ; Medicine and Health Sciences ; Mesothelioma ; Mesothelioma - diagnosis ; Munk, Peter ; Neural networks ; NMR ; Nuclear magnetic resonance ; Pain ; Patients ; Physicians ; Plaques ; Platelets ; Predictions ; Regression Analysis ; Research and Analysis Methods ; Risk analysis ; Risk factors ; Teaching methods
Is Part Of: PloS one, 2019-01, Vol.14 (1), p.e0208737-e0208737
Description: Mesothelioma is a lung cancer that kills thousands of people worldwide annually, especially those with exposure to asbestos. Diagnosis of mesothelioma in patients often requires time-consuming imaging techniques and biopsies. Machine learning can provide for a more effective, cheaper, and faster patient diagnosis and feature selection from clinical data in patient records. We analyzed a dataset of health records of 324 patients having mesothelioma symptoms from Turkey. The patients had prior asbestos exposure and displayed symptoms consistent with mesothelioma. We compared probabilistic neural network, perceptron-based neural network, random forest, one rule, and decision tree classifiers to predict diagnosis of the patient records. We measured classifiers' performance through standard confusion matrix scores such as Matthews correlation coefficient (MCC). Random forest outperformed all models tried, obtaining MCC = +0.37 on the complete imbalanced dataset and MCC = +0.64 on the under-sampled balanced dataset. We then employed random forest feature selection to identify the two most relevant dataset traits associated with mesothelioma: lung side and platelet count. These two risk factors resulted so predictive, that decision tree focusing on them achieved the second top accuracy on the complete dataset diagnosis prediction (MCC = +0.28), outperforming all other methods and even decision tree itself applied to all features. Our results show that machine learning can predict diagnoses of patients having mesothelioma symptoms with high accuracy, sensitivity, and specificity, in few minutes. Additionally, random forest can efficiently select the most important features of this clinical dataset (lung side and platelet count) in few seconds. The importance of pleural plaques in lung sides and blood platelets in mesothelioma diagnosis indicates that physicians should focus on these two features when reading records of patients with mesothelioma symptoms. Moreover, doctors can exploit our machinery to predict patient diagnosis when only lung side and platelet data are available.
Publisher: United States: Public Library of Science
Language: English
Identifier: ISSN: 1932-6203
EISSN: 1932-6203
DOI: 10.1371/journal.pone.0208737
PMID: 30629589
Source: GFMER Free Medical Journals
MEDLINE
PubMed Central
Public Library of Science (PLoS)
ProQuest Central
DOAJ Directory of Open Access Journals

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Computational prediction of diagnosis and feature selection on mesothelioma patient health records

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