By　the　end　of　2023,　China＇s　population　aged　60　and　above　is　projected　to　reach　296.97　million,　or　21.1%　of　the　total　population,　with　those　aged　65　and　above　numbering　216.76　million,　accounting　for　15.4%.　This　marks　China＇s　transition　into　a　moderately　aged　society.　Addressing　the　diverse　needs　of　the　elderly　and　solving　the　social　issues　arising　from　population　aging　are　crucial　for　the　nation＇s　overall　development　and　public　welfare.　A　comprehensive　analysis　of　the　living　environment　of　the　elderly　can　aid　in　enhancing　care　services　for　this　group.　This　study　analyzes　data　from　home-tested　and　hospitalized　elderly　patients　to　explore　differences　across　various　indicators.　It　classifies　patients　based　on　factors　such　as　stress　test　comprehensive　index,　self-rating　stress　scale　(SDS　score),　age,　and　gender,　evaluating　model　performance　using　metrics　like　accuracy　and　recall.　Principal　component　analysis　(PCA)　and　K-means　clustering　have　revealed　key　differences　between　the　two　groups,　while　Random　Forest,　Support　Vector　Machine　(SVM),　and　XGBoost　were　employed　for　classification　modeling　to　predict　whether　elderly　patients　are　at　home　or　in　hospital.　The　results　indicate　significant　differences　in　the　feature　space　between　the　two　patient　groups,　with　feature　analysis　accurately　predicting　patient　classification.　This　study　offers　valuable　data　support　for　elderly　health　management,　suggesting　that　future　research　should　consider　more　complex　models　or　additional　features　to　improve　prediction　accuracy　and　robustness.　The　findings　highlight　the　importance　of　tailored　care　strategies　to　address　the　diverse　needs　of　an　aging　population,　ultimately　contributing　to　better　healthcare　outcomes　for　the　elderly.