Binary Classification of Malignant Mesothelioma: A Comparative Study

Cheng, Ted Si Yuan; Liao, Xiyue

doi:10.6339/23-JDS1090

Journal of Data Science

Binary Classification of Malignant Mesothelioma: A Comparative Study

Volume 21, Issue 2 (2023): Special Issue: Symposium Data Science and Statistics 2022, pp. 205–224

Ted Si Yuan Cheng Xiyue Liao

https://doi.org/10.6339/23-JDS1090

Pub. online: 14 February 2023 Type: Data Science In Action

Open Access

Received
25 July 2022

Accepted
6 February 2023

Published
14 February 2023

Abstract

Malignant mesotheliomas are aggressive cancers that occur in the thin layer of tissue that covers most commonly the linings of the chest or abdomen. Though the cancer itself is rare and deadly, early diagnosis will help with treatment and improve outcomes. Mesothelioma is usually diagnosed in the later stages. Symptoms are similar to other, more common conditions. As such, predicting and diagnosing mesothelioma early is essential to starting early treatment for a cancer that is often diagnosed too late. The goal of this comprehensive empirical comparison is to determine the best-performing model based on recall (sensitivity). We particularly wish to avoid false negatives, as it is costly to diagnose a patient as healthy when they actually have cancer. Model training will be conducted based on k-fold cross validation. Random forest is chosen as the optimal model. According to this model, age and duration of asbestos exposure are ranked as the most important features affecting diagnosis of mesothelioma.

Supplementary material

Supplementary Material

The zip supplementary material file contains the Python and R scripts for reading data and preprocessing, exploratory data analysis, and the various models tested.

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2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.

Open access article under the CC BY license.

Keywords

binary classification cancer class imbalance machine learning mesothelioma variable importance

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