Hypertension　is　one　of　the　most　common　chronic　diseases　threatening　human　health,　and　early　warning　and　intervention　are　crucial　for　controlling　disease　progression.　However,　in　the　real　world,　the　sample　data　used　for　hypertension　model　training　is　often　limited,　posing　challenges　to　model　performance.　To　address　this　issue,　this　paper　proposes　an　end-to-end　hypertension　early　warning　model　based　on　Generative　Adversarial　Networks　(GANs)　and　Long　Short-Term　Memory　(LSTM)　networks,　which　can　generate　a　large　number　of　high-quality　synthetic　electronic　health　records(EHRs)　in　a　small　sample　environment　and　directly　use　them　for　training　the　hypertension　early　warning　model.　Specifically,　we　use　the　processed　data　from　the　public　MIMIC-III　dataset　as　input　and　generate　a　large　number　of　synthetic　EHRs　through　GAN　and　LSTM　networks.　The　GAN　network　generates　realistic　synthetic　data　through　adversarial　training　of　the　discriminator　and　generator,　while　the　LSTM　network　is　used　to　capture　time　series　features,　thereby　enhancing　the　authenticity　and　diversity　of　the　data.　After　generating　the　synthetic　data,　these　data　are　directly　used　to　train　the　hypertension　early　warning　model.　The　feedback　mechanism　during　the　generation　of　EHRs　can　continuously　obtain　higher　prediction　accuracy,　thus　optimizing　the　quality　of　the　generated　data　until　the　optimal　effect　is　achieved.　Finally,　the　optimal　hypertension　early　warning　model　and　the　corresponding　synthetic　data　are　saved.　Experimental　results　show　that　the　hypertension　early　warning　model　trained　using　synthetic　data　significantly　improves　the　prediction　accuracy　on　the　test　set,　with　higher　sensitivity　and　specificity　compared　to　traditional　methods.　This　study　verifies　the　effectiveness　and　superiority　of　the　proposed　method　in　a　small　sample　environment,　providing　a　new　perspective　for　the　utilization　of　large-scale　medical　data,　and　has　a　wide　range　of　application　prospects.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.