  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.
