A Comparison of Machine Learning Algorithms in Predicting Pulmonary diseases

Smart cities are becoming increasingly significant as a result of global urbanization and the drive for more sustainable and livable communities. A smart city’s network of interconnected heterogeneous devices generates a plethora of data. Furthermore, it is generally known that air pollution exists all around us as a result of numerous automobiles on the road, stubble burning or fire, and industrial air pollution; this is detrimental to our health, particularly our lungs. Therefore it is important to predict lung diseases very accurately and precisely. In this paper, three algorithms are used “Naïve Bayes”, “(Support Vector Machine) SVM”, and “(K-Nearest neighbor) K-NN” Algorithms to predict lung diseases based on the symptoms. The accuracy percentage of each algorithm is calculated, which states that K-NN is better than both the algorithms when the number of clusters taken is equal to or greater than six.