Performance Evaluation of Various Machine Learning Algorithms for Lung Cancer Prediction Using Demographic Data

Lung cancer is among the top deadly diseases, affecting human beings globally. Therefore, it is crucial to predict and detect this disease as early as possible, allowing the doctors and the patients to take the appropriate and essential actions. Techniques like machine learning can be applied to the same. In this study, we used machine learning to predict cancer in the lungs. We explored five machine learning algorithms, viz., Decision Tree (DT), Support vector machine (SVM), Naive Bayes (NB), Logistic Regression (LR), and Random Forest (RF). A publicly available dataset that contains demographic information of 284 patients with 16 parameters is used to conduct this study. An extensive explorative analysis is performed to improve the quality assessment of the dataset. Among five algorithms, Logistic Regression (LR) exhibited best findings in terms of accuracy, precision, recall, specificity, f1-score, and negative predicted values (NPV). Compared to similar research works, the proposed model achieved better results based on various performance evaluation metrics. The proposed model can be used for other illnesses that have similar symptoms by using transfer learning approach.