Early Lung Disease Detection with Novel K -Nearest Neighbour Against Genetic Algorithm for Better Accuracy

Proposed model is compared by using Novel K-NN algorithm to the Genetic Algorithm in order to increase lung cancer prediction at early stage. For detection and remedy for lung cancer identification, the proposed effort involves two groups. The K-NN was in Group 1, while the GA was in Group 2. There were 20 samples each group and a total of 40 samples taken. For enhancing accuracy, the revised K-NN method was compared to the Genetic Algorithm (GA). The Dataset taken for this research work from Kaggle website with the latest event logs with various timestamp. The Novel K-NN algorithm was compared to the GA approach in this suggested work. When compared GA, K-NN showed a much higher accuracy rate (92.02 percent) compared to GA (67.85 percent) has a significance level of 0.05 (p<0.05). The information was gathered from a variety of cancer in lung in predicting systems. For a higher accuracy than the GA, the upgraded K-NN algorithm was utilised for the entire lung cancer prediction procedure.