Paper Info
Title
Identification of Cancer - Mesothelioma Disease Using Logistic Regression and Association Rule.
Abstract
Malignant Pleural Mesothelioma (MPM) or malignant mesothelioma (MM) is an atypical, aggressive tumor that matures into cancer in the pleura, a stratum of tissue bordering the lungs. Diagnosis of MPM is difficult and it accounts for about seventy-five percent of all mesothelioma diagnosed yearly in the United States of America. Being a fatal disease, early identification of MPM is crucial for patient survival. Our study implements logistic regression and develops association rules to identify early stage symptoms of MM. We retrieved medical reports generated by Dicle University and implemented logistic regression to measure the model accuracy. We conducted (a) logistic correlation, (b) Omnibus test and (c) Hosmer and Lemeshow test for model evaluation. Moreover, we also developed association rules by confidence, rule support, lift, condition support and deployability. Categorical logistic regression increases the training accuracy from 72.30% to 81.40% with a testing accuracy of 63.46%. The study also shows the top 5 symptoms that is mostly likely indicates the presence in MM. This study concludes that using predictive modeling can enhance primary presentation and diagnosis of MM.
Year
DOI
Venue
2018
10.3844/ajeassp.2018.1310.1319
American Journal of Engineering and Applied Sciences
DocType
Volume
Issue
Journal
abs/1812.10384
4
ISSN
Citations 
PageRank 
American Journal of Engineering and Applied Sciences 2018, 11(4):1310.1319
2
0.42
References 
Authors
0
1
Name
Order
Citations
PageRank
Avishek Choudhury152.92