A novel framework for prognostic factors identification of malignant mesothelioma through association rule mining

• Published in: Biomedical signal processing and control 2021-07, Vol.68, p.102726, Article 102726
• Main Authors: Alam, Talha Mahboob, Shaukat, Kamran, Hameed, Ibrahim A., Khan, Wasim Ahmad, Sarwar, Muhammad Umer, Iqbal, Farhat, Luo, Suhuai
• Format: Article
• Language: English
• Subjects: Association rule mining; Feature selection; Malignant mesothelioma; Prognostic factors; Rule extraction
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.102726

•A novel framework based on association rule mining has been proposed for prognostic factors identification of malignant mesothelioma.•We compared the performance of the proposed framework with state-of-the-art methods to validate the findings.•Asbestos exposure, CRP, pleural albumin, pleural LDH, and pleural effusion are significant prognosis factors.•The proposed framework may assist doctors, patients, and medical practitioners for early diagnosis and better treatment. Malignant mesothelioma (MM) is a rare cancer type arising from mesothelial cells. The current clinical diagnosis is based on contrast-enhanced computed tomography, magnetic resonance imaging, and positron emission tomography that are either invasive or costly. The failure to diagnose malignantly can lead to an increased risk of multiple medical conditions, including cardiovascular diseases, emotional distress, anemia, and diabetes. To date, there is a limited number of prognostic factors that can be used for diagnosis. Most existing work has considered the MM disease as a classification task. In contrast, our study has initiated a knowledge extraction problem and proposed a machine learning-based framework. The performance status, age, and sex of patients are currently the most substantial clinical prognostic factors, but other histopathological and clinical prognostic factors are still unclear. This study aims to search for clinical prognostic, radiological, and histopathological factors in MM. In this study, the latest dataset from a public repository (UCI) has been utilised, including patients' medical, socio-economic, histopathological, and clinical factors. Association rule mining-based algorithms (Apriori and frequent pattern (FP) growth method) and feature selection techniques have been employed to extract significant features. The performance of the proposed framework has been evaluated based on support, confidence, and lift. We set the support, confidence, and lift between 0.5–1.0, 0.5–1.0, and 1.0–1.6 respectively. Our results showed five significant prognosis factors with the values for the identification of MM: Pleural lactate dehydrogenase >500 IU/L, C-reactive protein >10/μL, pleural albumin