Lung Cancer is the major cause of mortality that is cancer-related. Therefore, the diagnosis, prediction and detection of this disease have become very important. Machine Learning techniques have been in use for the medication of such conditions because of their high accuracy results. The aim is to use machine learning based techniques for lung cancer prediction. The analysis of dataset is done with the help of Supervised Machine Learning Techniques (SMLT) to capture several information like, variable identification, uni-variate analysis, bivariate and multivariate analysis, missing value treatments and analyze the data validation, data cleaning and data visualization which will be done on the entire given dataset. For the prognosis and analysis of lung cancer in the healthcare sector, various machine learning algorithms have been utilized such as Artificial Neural Networks (ANN), Naive Bayes, Logistic Regression and Support Vector Machines (SVM). Applications of machine learning algorithms are discussed in this report. The advantages and disadvantages of these algorithms are also discussed. The experimental study proved that the proposed model is a highly optimized model of existing machine learning algorithms.
M et al. (Wed,) studied this question.