Prediction of Lungs Cancer Diseases Datasets Using Machine Learning Algorithms

Lung cancer is the most common cause of mortality, and it is the only sort of cancer that affects both men and women globally. The primary goal of this paper is to creates a model for predicting lungs cancer using various machine learning classification algorithms like k Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Gaussian Naive Bayes (NB). Furthermore, assess and compare the performance of the varied classifiers using their accuracy in selecting the best algorithms. The lung cancer dataset is publicly available on the Kaggle Machine Learning Repository, thus the implementation phase dataset will be partitioned as 80% for the training phase and 20% for the testing phase before using machine learning methods. In all parameters, the support vector machine performed well.

Lung cancer is the most common cause of mortality, and it is the only sort of cancer that affects both men and women globally. The primary goal of this paper is to creates a model for predicting lungs cancer using various machine learning classification algorithms like k Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Gaussian Naive Bayes (NB). Furthermore, assess and compare the performance of the varied classifiers using their accuracy in selecting the best algorithms. The lung cancer dataset is publicly available on the Kaggle Machine Learning Repository, thus the implementation phase dataset will be partitioned as 80% for the training phase and 20% for the testing phase before using machine learning methods. In all parameters, the support vector machine performed well.