Diagnosis of pleural mesothelioma using machine learning

Abstract

Mesothelioma is cancer that develops in the pleura. The most common cause of this disease is contact with asbestos. Patients with mesothelioma have a better chance of surviving if they are diagnosed quickly. This study utilizes a variety of machine learning to enhance pleural mesothelioma diagnosis. The possibility of misclassification was decreased by extracting features from a preexisting dataset. SVM, Decision Trees, and Random Forests are only a few machine learning classifiers trained using essential and foundational features. Accuracy, precision, recall, and F1-score were just a few measures used to evaluate these classifiers' performance in cross- validation. SVM demonstrated excellent accuracy, precision, recall, and F1-score when classifying individuals as either healthy or having mesothelioma. The results show the potential of machine learning techniques for early diagnosis of pleural mesothelioma. Machine learning algorithms improve diagnosis accuracy and turnaround time, improving patient outcomes. Using the results of this research, a fully automated technique for diagnosing mesothelioma might be developed, allowing clinicians more time to provide better care for their patients.