A novel fuzzy expert system design to assist with peptic ulcer disease diagnosis.

Peptic ulcer disease causes abdominal discomfort and pain. Helicobacter pylori bacteria, infection and long-term use of anti-inflammatory drugs are the most common causes of peptic ulcers. Untreated ulcers can lead to other more serious health complications. Gastric cancer is the second commonest cause of death from malignant disease. Speed performance is always quietly essential in detecting and treating peptic ulcer. Combining artificial intelligence with medical knowledge provides a faster and more accurate diagnosis. The main purpose of this study is to design a novel, inexpensive, reliable and quick Fuzzy Expert System for diagnosis of peptic ulcer disease. A data set of 101 Male adult Wistar rats with a weight range of 200–250 g were obtained from the Pasteur institute. Peptic ulcer induced by Indomethacin (50 mg/kg, 2 ml). A computational approach based on a Fuzzy Inference System (FIS) is suggested in this study for the evaluation of peptic ulcer. The Fuzzy Inference System was produced with Fuzzy C-Means and tuned using the Adaptive Neuro-Fuzzy Inference System model (ANFIS). In order to compare the two methods, the performance of the FIS was evaluated with a ROC curve that prepares the FCM accuracy of 90% and ANFIS accuracy of 85%. In conclusion, the Fuzzy Expert System can potentially increase the accuracy and efficiency of medical practices for peptic ulcer diseases to move towards more precision medicine and treatment. This applicable Fuzzy system may impact hospitals and health systems in improving efficiency and productivity, while reducing the cost of care. It can also be considered in medicine to reduce manual tasks and human error. [ABSTRACT FROM AUTHOR]