This paper proposes a neural fuzzy evaluation system (NFES) with significant variables selected from stepwise regression to predict apnea-hypopnea index (AHI) for evaluating obstructive sleep apnea (OSA). The variables considered are the change statuses of blood pressure (BP) before going to sleep and early in the morning as well as other five easily available measurements (age, body mass index (BMI), etc.) so that users can use the system for self-evaluation of OSA. A total of 150 subjects are reviewed retrospectively and categorized as training (120 subjects) and validation (30 subjects) sets by a fivefold cross-validation scheme with stratified sampling based on the OSA severity. Among the eight variables, the stepwise regression shows that BMI, the difference of systolic BP, and Epworth Sleepiness Scale were the significant factors to predict AHI. The three variables are fed as inputs to the NFES with interpretable fuzzy rules automatically generated from the training set. The average accuracy, sensitivity (Sn), specificity (Sp), and Sn+Sp-1 of the NFES were 75.6%, 77.2%, 75.0%, and 0.552, respectively, in distinguishing the OSA level of normal-mild (AHI <15) from moderate-severe (AHI ≱ 15), and outperformed the stepwise regression, back-propagation neural network, and support vector machine models. In addition to personal self-estimation, physicians could differentiate the two OSA levels by means of the fast-screening system for both outpatients and inpatients.