Personalized Medical Diagnosis Recommendation Based on Neutrosophic Sets and Spectral Clustering

With the development of cloud-based services and artificial intelligence technologies, the personalized diagnosis recommender system has been a hot research topic in medical services. An effective diagnosis recommendation model could help doctors and patients make more accurate predictions in clinical diagnosis. In this paper, we propose a novel personalized diagnosis recommendation method based on neutrosophic sets, spectral clustering, and web-based medical information to offer satisfied web-based medical service. Firstly, the neutrosophic set theory is adopted to formulate the patients’ personal information and the symptom features into more interpretable neutrosophic sets with uniformly normalized values. Moreover, to make more accurate predictions, the spectral clustering scheme is integrated into a neutrosophic-based prediction approach to mining the similarity relationships between the undiagnosed diseases and the history disease records. Finally, a deneutrosophication operation is applied to recommend the final fine-grain diagnoses with interpretable clinic meanings. Experimental results on four real-world medical diagnosis datasets validate the effectiveness of the proposed method.