Research Article Open Access

Identification of Cancer: Mesothelioma’s Disease Using Logistic Regression and Association Rule

Avishek Choudhury1
  • 1 Syracuse University, United States
American Journal of Engineering and Applied Sciences
Volume 11 No. 4, 2018, 1310-1319

DOI: https://doi.org/10.3844/ajeassp.2018.1310.1319

Submitted On: 2 November 2018 Published On: 7 December 2018

How to Cite: Choudhury, A. (2018). Identification of Cancer: Mesothelioma’s Disease Using Logistic Regression and Association Rule. American Journal of Engineering and Applied Sciences, 11(4), 1310-1319. https://doi.org/10.3844/ajeassp.2018.1310.1319

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.

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Keywords

  • Logistic Regression
  • Mesothelioma
  • Predictive Modeling
  • Cancer Detection
  • Association Rules