Use of an artificial intelligence-based rule extraction approach to predict an emergency cesarean section

Abstract

Objective: One of the major problems with artificial intelligence (AI) is that it is generally known as a "black box". Therefore, the present study aimed to construct an emergency cesarean section (CS) prediction system using an AI-based rule extraction approach as a "white box" to detect the cause for the emergency CS.

Methods: Data were collected from all perinatal records of all delivery outcomes at Osaka Medical College between December 2014 and July 2019. We identified the delivery method for all deliveries after 36 gestational weeks as either (1) vaginal delivery or scheduled CS, or (2) emergency CS. From among these, we selected 52 risk factors to feed into an AI-based rule extraction algorithm to extract rules to predict an emergency CS.

Results: We identified 1513 singleton deliveries (1285 [84.9%] vaginal deliveries, 228 emergency CS [15.1%]) and extracted 15 rules. We achieved an average accuracy of 81.90% using five-fold cross-validation and an area under the receiving operating characteristic curve of 71.46%.

Conclusion: To our knowledge, this is the first study to use interpretable AI-based rule extraction technology to predict an emergency CS. This system appears to be useful for identifying hidden factors for emergency CS.

Keywords: artificial intelligence; delivery; emergency cesarean section; predictive decision system; rule extraction.

FIGURE 1

Risk levels of the five‐tier classification system as defined by the Japanese Society of Obstetrics and Gynecology. 1, Level 1 Normal pattern; 2, Level 2 Benign variant pattern; 3, Level 3 Mild variant pattern; 4, Level 4 Moderate variant pattern; 5, Level 5 Severe variant pattern [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 2

Schematic overview of Re‐RX with J48graft. Abbreviations: CS, cesarean section; NN, neural network [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 3

CONSORT flowchart