Predictive model for novel subtypes of patients undergoing lower extremity amputation for peripheral artery disease: An unsupervised machine learning study.

ABSTRACT:

Background

Peripheral artery disease (PAD) represents the frequently seen circulatory condition related to a risk of critical limb ischemia and amputation. Critical lower extremity ischemia may require amputation, and the outcomes vary. In this study, we developed an artificial intelligence (AI)-driven predictive model for PAD subtypes to assess risk among patients more precisely and accurately to predict disease progression.

Methods

The present retrospective study examined clinical data in PAD patents undergoing lower extremity amputation. The data were analyzed using an unsupervised machine learning algorithm (UMLA) for subgroup identification and risk stratification. The clustering result accuracy was validated by analyzing the follow-up data of clusters. Finally, we buil

SUBMITTER: Ma Y 

PROVIDER: S-EPMC11327519 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature