Lung Diseases Classification Using the Naïve Bayes Algorithm

Abstract

Lung disease is one of the diseases with a high rate of spread and mortality, especially in developing countries. Early detection is very important to increase the chances of recovery. This study aims to classify the types of lung disease using Naive Bayes, a probability-based statistical classification method. We gathered the dataset that includes common symptoms of lung disease, such as chronic cough, shortness of breath, chest pain, and others. The results of the study showed that Naive Bayes can achieve a fairly high classification accuracy of 87%. These results indicate that Naive Bayes can be an effective approach to support medical decisions.