Diagnosis of Pain in the Right Iliac Fossa. A New Diagnostic Score Based on Decision-Tree and Artificial Neural Network Methods

Abstract Introduction Pain in the right iliac fossa (RIF) continues to pose diagnostic challenges. The objective of this study is the development of a RIF pain diagnosis model based on classification trees of type CHAID (Chi-Square Automatic Interaction Detection) and on an artificial neural network (ANN). Methods Prospective study of 252 patients who visited the hospital due to RIF pain. Demographic, clinical, physical examination and analytical data were registered. Patients were classified into 4 groups: NsP (nonspecific RIFP group), AA (acute appendicitis), NIRIF (RIF pain with no inflammation) and IRIF (RIF pain with inflammation). A CHAID-type classification tree model and an ANN were constructed. The classic models (Alvarado [ALS], Appendicitis Inflammatory Response [AIR] and Fenyo-Lindberg [FLS]) were also evaluated. Discrimination was assessed using ROC curves (AUC [95% CI]) and the correct classification rate (CCR). Results 53% were men. Mean age 33.3 ± 16 years. The largest group was the NsP (45%), AA (37%), NRIF (12%) and IRIF (6%). The analytical model results were: ALS (0.82 [0.76–0.87]), AIR (0.83 [0.77–0.88]) and FLS (0.88 [0.84–0.92]). CHAID determined 10 decision groups: 3 with high probability for NsP, 3 high for AA and 4 special groups with no predominant diagnosis. CCR of ANN and CHAID were 75% and 74.2%, respectively. Conclusions The methodology based on CHAID-type classification trees establishes a diagnostic model based on four pain groups in RIF and generates decision rules that can help us in the diagnosis of processes with RIF pain.