- Title
- Risk factors identification of malignant mesothelioma: a data mining based approach
- Creator
- Latif, Muhammad Zohaib; Shaukat, Kamran; Luo, Suhuai; Hameed, Ibrahim A.; Iqbal, Farhat; Alam, Talha Mahboob
- Relation
- 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). Proceedings of 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) (Istanbul, Turkey 12-13 June, 2020)
- Publisher Link
- http://dx.doi.org/10.1109/ICECCE49384.2020.9179443
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2020
- Description
- A significant type of Lung cancer today know to the world is called Malignant Mesothelioma (MM). MM is associated with an inferior prognosis, and the majority of patients do not show symptoms. The etiology of MM is essential for the identification of disease. Clinical results provide a better way for the treatment of disease. Typically, costly imaging and laboratory resources, i.e. (X-rays, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) scans, biopsies, and blood tests) are required for the identification of MM risk factors. Furthermore, these methods are often expensive and invasive. The primary purpose of this work is to explore risk factors of MM. 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, erythrocyte sedimentation rate, duration of time for asbestos exposure and Pleural to serum LDH ratio determined via Apriori algorithm. The identification of risk factors associated with MM may prevent patients from going into the high danger of the disease. This will also help to control the comorbidities associated with MM, which are cardiovascular diseases, cancer-related emotional distress, diabetes, anemia, and hypothyroidism.
- Subject
- bioinformatics; malignant mesothelioma; etiological factors; data mining; association rule mining; imbalanced dataset; SDG 3; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1471349
- Identifier
- uon:48655
- Identifier
- ISBN:9781728171166
- Language
- eng
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