Lung Cancer Diagnosis: A Comparative Analysis of Machine Learning Algorithms

Lung pain is one of the most common complaints during the early stages of cancer therapy, and the most difficult aspect is waiting for the radiologist’s diagnosis. For radiologists, a high-tech computerized system is definitely very helpful. Many machine learning-based studies are available for lung cancer diagnosis. Lung cancer is strongly predicted using a multistage bracket. For data enhancement, the double classifier and threshold and marke controlled watershed used in the bracket system’s segmentation system are applied. Lung cancer is an extremely difficult disease to identify. Techniques like Support Vector Machine, K Nearest Neighbour, Decision Tree, Logistic Regression, Naïve Bayes, and Random Forest are used to train the dataset, and it is shown that these algorithms show sophisticated delicacy. The best accuracy of 96.1% is obtained by K-Fold Validation with SVM and Logistic Regression, according to a comparative study of the experimental data.