Lung – Pleura Carcinoma Detection Using Machine Learning

Abstract

Identification of pleural carcinoma using classification equipment with 98.30% accuracy is presented in this work. To evaluate the effectiveness of the Machine Learning Algorithms, which is divided into clinical, and health data from patients who were part of the collection of lung cancer diagnostic data. These algorithms used to predict and analyze the effectiveness of various machine-learning algorithms associated with lung disease based on medical and patient health data and to guide patients and physicians in early detection or early treatment options. Separation processes are performed with different machine learning algorithms and success levels are indicated. Various algorithms were tested to achieve success rates of approximately 98.30% obtained. Among the tried algorithms, Linear Discriminant Analysis provides the most effective isolation process.