Computational prediction of diagnosis & feature selection on mesothelioma patient records | AISC

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Toronto Deep Learning Series - Authors Stream

Presenter & author: Davide Chicco ( http://www.DavideChicco.it )

Facilitators: Nassim Tayari & Shazia Akbar

Paper: https://doi.org/10.1371/journal.pone.0208737

Event details: https://tdls.a-i.science/events/2019-02-19

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

"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.
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."



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Tags:
machine learning
healthcare