探索心脏疾病智能预测:人工智能体与文本嵌入模型的综合应用OA

Objective To develop an agent-based heart disease prediction(ABHDP)model that combines multiple machine learning classifiers with text embedding model to improve prediction performance and contextual interpretation in heart disease risk assessment.Methods The proposed ABHDP model integrates three functional modules:disease classification,medical knowledge retrieval,and interactive interpretation.A heart disease dataset was used together with medical knowledge dataset.Eleven classification algorithms and three text embedding approaches were evaluated.Model performance was evaluated using accuracy,precision,recall,F1-score,and receiver operating characteristic(ROC).Results Analysis of categorical and continuous variables revealed important patterns associated with the prevalence of heart disease.Among the classification algorithms,CatBoost and random forest achieved the highest recall values(0.980 and 0.979,respectively).For text embedding methods,FastText produced an F1-score of 46%,while SBERT and OpenAI achieved substantially higher scores of 95%and 96%.Conclusion The proposed ABHDP model demonstrates the potential to support interpretable and intelligent clinical decision-making for heart disease risk prediction,thus promoting the development of precision healthcare.