Decision support system for diagnosing asthma disease

Abstract

Field of medicine is more about decision making. The role of a physician is more pivotal in the diagnosis of a disease from all other possible illnesses. This is called as “Differential Diagnosis” in the field of medicine where the key aspect is the analysis of symptoms. But, due to both physicians’ factors and patient’s factors there occurs considerable amount of diagnostic errors which lead to dangerous consequences. So, there’s a rising concern in reducing these errors in medical diagnosis worldwide. When it comes to respiratory diseases differential diagnosis is more challenging due to the commonness of symptoms of various diseases and also the connection of cardiac diseases. Proper diagnosis needs both theoretical knowledge and the knowledge comes from experience. If we can blend both types into one place it will definitely support to come to more appealing conclusions when diagnosing diseases. Due to the nature of vagueness in expressions in the medical field, a technology which can cope with this gray area is more suitable. Fuzzy logic is a type of logic that identifies more than simple true and false values and can be represented with degrees of truthfulness and falsehood. Fuzzy logic is being integrated in many experts systems to solve many real world problems. Through our project we have developed a decision support system for diagnosing Asthma (a respiratory disease) and its stages in adults using fuzzy logic. The system is built based on 21 inputs which were considered by the expert as the most important symptoms and laboratory tests in diagnosing Asthma and its severity stages. The system proves its ability of addressing the problems stated above thus can be relied upon and further improved for coverage of more diseases.