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

ABSTRACT:

Background

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.

Methods and findings

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 measure

SUBMITTER: Chicco D 

PROVIDER: S-EPMC6328132 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature