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Malignant Pleural Mesothelioma (MPM) or malignant mesothelioma (MM) is anatypical, aggressive tumor that matures into cancer in the pleura, a stratum oftissue bordering the lungs. Diagnosis of MPM is difficult and it accounts forabout seventy-five percent of all mesothelioma diagnosed yearly in the UnitedStates of America. Being a fatal disease, early identification of MPM iscrucial for patient survival. Our study implements logistic regression anddevelops association rules to identify early stage symptoms of MM. We retrievedmedical reports generated by Dicle University and implemented logisticregression to measure the model accuracy. We conducted (a) logisticcorrelation, (b) Omnibus test and (c) Hosmer and Lemeshow test for modelevaluation. Moreover, we also developed association rules by confidence, rulesupport, lift, condition support and deployability. Categorical logisticregression increases the training accuracy from 72.30% to 81.40% with a testingaccuracy of 63.46%. The study also shows the top 5 symptoms that is mostlylikely indicates the presence in MM. This study concludes that using predictivemodeling can enhance primary presentation and diagnosis of MM.

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