RESUMEN
BACKGROUND: Peripheral artery disease (PAD) is a common syndrome in elderly people. Recently, artificial intelligence (AI) algorithms, in particular machine-learning algorithms, have been increasingly used in disease diagnosis. AIM: In this study, we designed an effective diagnostic model of PAD in the elderly patients using artificial intelligence. METHODS: The study was performed with 539 participants, all over 80 years in age, who underwent the measurements of Doppler ultrasonography and ankle-brachial pressure index (ABI). Blood samples were collected. ABI and two machine-learning algorithms (MLAs)-logistic regression and a random forest (RF) model-were established to diagnose PAD. The sensitivity and specificity of the models were analyzed. An additional RF model was designed based on the most significant features of the original RF model and a prospective study was conducted to demonstrate its external validity. RESULTS: Thirteen of the 28 features introduced to the MLAs differed significantly between PAD and non-PAD participants. The respective sensitivities and specificities of logistic regression, RF, and ABI were as follows: logistic regression (81.5%, 83.8%), RF (89.3%, 91.6%) and ABI (85.1%, 84.5%). In the prospective study, the newly designed RF model based on the most significant seven features exhibited an acceptable performance rate for the diagnosis of PAD with 100.0% sensitivity and 90.3% specificity. CONCLUSIONS: An RF model was a more effective method than the logistic regression and ABI for the diagnosis of PAD in an elderly cohort.