Version 1
: Received: 7 November 2019 / Approved: 10 November 2019 / Online: 10 November 2019 (16:15:14 CET)
How to cite:
Alam, T.M. Identification of Malignant Mesothelioma Risk Factors through Association Rule Mining. Preprints2019, 2019110117. https://doi.org/10.20944/preprints201911.0117.v1
Alam, T.M. Identification of Malignant Mesothelioma Risk Factors through Association Rule Mining. Preprints 2019, 2019110117. https://doi.org/10.20944/preprints201911.0117.v1
Alam, T.M. Identification of Malignant Mesothelioma Risk Factors through Association Rule Mining. Preprints2019, 2019110117. https://doi.org/10.20944/preprints201911.0117.v1
APA Style
Alam, T.M. (2019). Identification of Malignant Mesothelioma Risk Factors through Association Rule Mining. Preprints. https://doi.org/10.20944/preprints201911.0117.v1
Chicago/Turabian Style
Alam, T.M. 2019 "Identification of Malignant Mesothelioma Risk Factors through Association Rule Mining" Preprints. https://doi.org/10.20944/preprints201911.0117.v1
Abstract
Malignant mesothelioma is a rare proliferative cancer that develops in the thin layer of tissues surrounding the lungs. Malignant mesothelioma is associated with an extremely poor prognosis and the majority of patients do not show symptoms. The epidemiology of mesothelioma is important for the identification of disease. The primary aim of this study is to explore the risk factors associated with mesothelioma. The dataset consists of healthy and mesothelioma patients but only mesothelioma patients were selected for the identification of symptoms. The raw data set has been pre-processed and then the Apriori method was utilized for association rules with various configurations. The pre-processing task involved the removal of duplicated and irrelevant attributes, balanced the dataset, numerical to the nominal conversion of attributes in the dataset and creating the association rules in the dataset. Strong associations of disease’s factors; asbestos exposure, duration of asbestos exposure, duration of symptoms, erythrocyte sedimentation rate and Pleural to serum LDH ratio determined via Apriori algorithm. The identification of risk factors associated with mesothelioma may prevent patients from going into the high danger of the disease. This will also help to control the comorbidities associated with mesothelioma which are cardiovascular diseases, cancer-related emotional distress, diabetes, anemia, and hypothyroidism.
Keywords
malignant mesothelioma; epidemiology; association rule mining; Apriori method; imbalanced dataset
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
