A Computational Intelligence-based Early Diagnosis of Asthma Disease: A Saudi Arabian Case Study

According to research, an increase in chronic diseases, including asthma, has been identified. The number of asthma patients in Saudi Arabia is a cause for concern due to the weather conditions and lifestyle, especially in the post-pandemic era. It demands a solution to reduce infections by developing an intelligent system to detect asthma at an early stage, thereby preventing the disease or enabling early treatment. In this study, machine learning has been utilized to develop tools to track asthma symptoms at an early stage. Although there have been various prior attempts to apply machine learning to predict the occurrence of asthma. Nevertheless, focusing on the identification of the disease at the pre-symptom stage, particularly in the Saudi Arabian context, is a relatively neglected area. The dataset for the current study was obtained from King Fahad University Hospital, Dammam, Saudi Arabia, and included standard tests performed on patients, including blood tests, viral tests, and biochemistry tests. The dataset contains 17 significant attributes and includes information for 328 asthma patients: 165 are positive, and 163 are negative. The methods selected for application here are random forests (RF), artificial neural networks (ANN), support vector machines (SVM), and naive Bayes (NB). Each of these methods has been chosen based on its distinctive features. The experimental outcome revealed that the RF, SVM, and ANN approaches yielded 94%, which is the highest accuracy and improved upon the state of the art by 3.9%. It is worth noting that nine of the seventeen possible features were used to achieve the above accuracy. Despite RF, SVM, and ANN achieving the same accuracy, ANN has a higher error cost. Therefore, RF and SVM are superior based on the pattern of results, and hence they are suggested for this problem.