Diagnosis of Asthma Disease and The Levels using Forward Chaining and Certainty Factor

Abstract

Asthma disease is a major global health issue that affects at least 300 million people worldwide. Even for clinicians working in emergency rooms, predicting the severity of asthma is difficult. Predicting the intensity of an asthma attack is much more challenging because it is dependent on a number of factors, including the person's illness's features and severity. Forward Chaining and Certainty Factor algorithms can be implemented to diagnose the degree of asthma control, so the consultation process through the system becomes more detailed. The expert system can be used as an initial reference for the diagnosis process. Forward Chaining algorithm is useful for reasoning, starting from a fact to a solution. On the other hand, Certainty Factor algorithm is used to provide a level of confidence from the conclusions by generating from Forward Chaining algorithm. The research implemented several phase as follow analysis, data preparation, modeling, and evaluation. On evaluation, this research conduct three stages and tested using 80 medical record data. The result of the study has produced an expert system and generated an accuracy level of 65%, the precision value of 58.3%, and recall also produced of 57.13%. Therefore, Chaining and Certainty Factor performs reasonably well in the diagnosis of asthma disease.

Optimization of Expert System Based on Interpolation, Forward Chaining, and Certainty Factor for Diagnosing Abdominal Colic

“…This will minimize the risk of analysis errors due to an inappropriate format. Forward chaining is a search technique that begins with information in the form of facts to test the truth of hypotheses and then attempts to draw conclusions (Abdillah and Innuddin, 2019;Irfan et al, 2022). This stage is useful for forming or describing rules that are derived from the collected data.…”