PSO-FCM based data mining model to predict diabetic disease. (November 2020)

• Record Type: Journal Article
• Authors: Raja, J. Beschi; Pandian, S. Chenthur
• Is Part Of: Computer methods and programs in biomedicine. Volume 196(2020)
• Publication Date: 2020-11
• Subjects: Data mining -- Type 2 diabetes mellitus (T2DM) -- Particle Swarm Optimization (PSO) -- Fuzzy Clustering Means (FCM) -- Sensitivity -- Specificity and accuracy; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105659
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A new model is proposed for forecasting type 2 diabetes mellitus (T2DM) based on data mining strategies. The purpose of the study is to enforce Particle Swarm Optimization (PSO) and Fuzzy Clustering Means (FCM) (PSO-FCM). The proposed method is to evaluate on a set of medical data relating to a diabetes diagnosis challenge. It was found that the prototype has achieved 8.26 percent more accuracy than the other methods. The proposed PSO-FCM method delivers greater performance when compared with other models. Abstract: Background and Objective: Diabetic disease is typically composed because of higher than normal blood sugar levels. Instead the production of insulin may be regarded insufficient. It has been noted in recent days that the percentage of diabetes-affected patients have grown to a larger extent throughout the world. Evidently, this problem must be taken more seriously in the coming days to ensure that the average percentages of diabetes-affected individuals are reduced. Recently, several research teams conducted detailed research on the data mining platform to determine the precision of each other. Data mining can be used by parametric modeling from the health data, including diabetic patient data sets, to synthesize expertise in the field. Methods: In this study, a new model is proposed for forecasting type 2 diabetes mellitus (T2DM) based on data mining strategies. The combined Particle Swarm Optimization (PSO) and Fuzzy Clustering Means (FCM) (PSO-FCM) are used to evaluate a set of medical data relating to a diabetes diagnosis challenge. Results: Experiments are performed on the Pima Indians Diabetes Database. The sensitivity, specificity and accuracy metrics widely used in medical studies have been used to assess the effectiveness of the proposed system reliability. It was found that the prototype has achieved 8.26 percent more accuracy than the other methods. Conclusion: The conclusion produced by using the method shows that, as compared with other models, the proposed PSO-FCM method delivers greater performance.