JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1090 10.6339/23-JDS1090 Data Science in Action Binary Classification of Malignant Mesothelioma: A Comparative Study ChengTed Si Yuanted.cheng@student.csulb.edu1∗ LiaoXiyue1 California State University, Long Beach, United States Corresponding author. Email: ted.cheng@student.csulb.edu. 20231422023212205224 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.

2572022622023 2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2023 Open access article under the CC BY license.

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.

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