Identification of Cancer -- Mesothelioma Disease Using Logistic Regression and Association Rule

TL;DR

This study applies logistic regression and association rule mining to early detection of malignant pleural mesothelioma, improving diagnostic accuracy and identifying key symptoms for early intervention.

Contribution

It introduces a combined approach of logistic regression and association rules for early MPM detection, with improved accuracy and symptom identification.

Findings

Training accuracy improved from 72.30% to 81.40%.

Testing accuracy achieved 63.46%.

Top 5 symptoms identified as indicators of MM.

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.