Abstract:
Diabetes is the 5th leading cause of death in the world, which threatens people's physical and mental health. However, in the course of studying diabetes, the missing med...Show MoreMetadata
Abstract:
Diabetes is the 5th leading cause of death in the world, which threatens people's physical and mental health. However, in the course of studying diabetes, the missing medical data has caused great trouble for researchers. There are few researches focusing on sparse medical data. Common method of filling missing values is not desirable. In this study, we select and analyze a large physical check dataset containing more than 900 records with missing data. Using these data, a model for predicting diabetes is designed and implemented. Before predicting, logistic regression is utilized to remain the most significant attributes as the input variables of prediction model. Concept lattice is considered an effective mathematical tool for conceptual data analysis and knowledge processing in mathematics. Experimental results demonstrate that concept lattice illustrates great capacity of predicting diabetes while remaining missing data.
Published in: 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD)
Date of Conference: 06-08 May 2019
Date Added to IEEE Xplore: 08 August 2019
ISBN Information: