Machine Learning Approach to Lung Cancer Survivability Analysis

Abstract

The majority of people in the current atmospheric conditions are affected by lung cancer disease. The analysis of respiratory illness offers a captivating and dynamic research space with far-reaching implications for human health. A diagnostics like this can only assist in reducing the likelihood of obtaining human life in jeopardy by initial detection of metastatic disease to address this problem. Lung cancer is the leading cause of cancer death worldwide, so different algorithms have been used to forecast the prognosis of cancer patients. Because of this, patients with lung cancer are living longer on average. When making predictions, the logistic regression assessment method is more accurate than that of other methods. This report examines two additional different approaches to machine learning for forecasting a lung participant's life expectancy, including linear discriminant analysis (LDA), random forest (RF), and artificial neural networks (ANN). In order to increase success rates, various algorithms were tested. The primary goal of this is to evaluate the accuracy of classification methodologies to develop a melanoma statistical method and a resilience analysis. The correctness, accuracy, recall, and selectivity of the numerous models’ performances are assessed and compared. In this enquiry, linear discriminant analysis will perform the best among the three algorithms.