Abstract
Lung cancer is a substantial health issue globally, and it is one of the main causes of mortality. Malignant mesothelioma (MM) is a common kind of lung cancer. The majority of patients with MM have no symptoms. In the diagnosis of any disease, etiology is crucial. MM risk factor detection procedures include positron emission tomography, magnetic resonance imaging, biopsies, X-rays, and blood tests, which are all necessary but costly and intrusive. Researchers primarily concentrated on the investigation of MM risk variables in the study. Mesothelioma symptoms were detected with the help of data from mesothelioma patients. The dataset, however, included both healthy and mesothelioma patients. Classification algorithms for MM illness diagnosis were carried out using computationally efficient data mining techniques. The support vector machine outperformed the multilayer perceptron ensembles (MLPE) neural network (NN) technique, yielding promising findings. With 99.87% classification accuracy achieved using 10-fold cross-validation over 5 runs, SVM is the best classification when contrasted to the MLPE NN, which achieves 99.56% classification accuracy. In addition, SPSS analysis is carried out for this study to collect pertinent and experimental data.