Machine Learning for Mesothelioma: Early Detection and Treatment

Mesothelioma is an extremely severe cancer that can easily transform into lung cancer. Mesothelioma diagnosis takes several months and treatment, including surgery, is expensive. Given the risk, early detection of Mesothelioma is essential for patient health, as it is connected to asbestos exposure. Various machine learning algorithms has been used in this research paper to compare with accurate results for mesothelioma detection. Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), k Nearest Neighbourhood (k-NN), and Linear Regression (LR) are some of the machine learning methods being used. For this research paper, dataset is available on UCI, called the University of California Irvine [1]. The test dataset contains 264 instances, 35 characteristics and 8 performance measures, which is used to evaluate the classifiers accuracy. The average accuracy of XGB, RF, DT, SGD, LR and Voting Classifier are 100% each. Combing all the classifiers, helps us to break through the Mesothelioma data and the creation of data driven insights to improve patient care.