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.
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Raja JB,Pandian SCdoi
10.1016/j.cmpb.2020.105659subject
Has Abstractpub_date
2020-11-01 00:00:00pages
105659eissn
0169-2607issn
1872-7565pii
S0169-2607(20)31492-9journal_volume
196pub_type
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