An Early Prognosis of Lung Cancer using Machine Intelligence

Cancer is a disease in which the body cells start growing uncontrollably and spreads all over the body. Mostly, the cancer symptoms appear only in the advanced stages. This disease is very complex in terms of its diagnosis in the early stages which results in a high mortality rate. Thus, there is a requirement for cancer to be diagnosed at its early stages which may result in better survival chances and the patients can be treated successfully. The dose-limiting toxicity in lung cancer radiotherapy (RT) is radiation pneumonitis (RP). Cancer characteristics and treatment features are intertwined, resulting, in RP associated with a single parameter is not always possible. This study aims to determine the algorithms which are most accurate for lung cancer prediction. As per the study by WHO, it has been found that in the year 2020, a total of 2.21 million people were diseased with lung cancer resulting in 1.80 million deaths all over the globe. In India, each year almost 70,000 active cases of lung cancer are identified. Early detection plays an important role in saving lives because it can give a patient a better chance to cure and recover. In recent times, different computer technologies are used for solving the problems of cancer detection. In this work, several types of machine-learning algorithms such as Naive Bayes (accuracy 96.61%), Decision tree (accuracy 91.52%), Random forest (accuracy 93.22%), Logistic Regression (accuracy 96.61%), Multilayer perceptron (accuracy 98.30%) have been utilized for predicting lung cancer. Among all of these algorithms, multilayer perceptron is the best algorithm to diagnose lung cancer.