• Anoop Kumar Paharia

Diabetes might be an infection that influences a considerable lot of us these days. The greater part of the exploration is happening around there. during this paper, we proposed a model to disentangle the issues in the current framework in applying information preparing strategies to be specific bunching and orders which are applied to analyze such a diabetes and its seriousness level for every persistent from the data gathered. This paper attempts to analyze diabetes upheld the 650 patient's information with which we investigated and distinguished the seriousness of diabetes. As a piece of the system Basic k-implies calculation is utilized for bunching the entire dataset into 3 groups i.e., bunch 0 - for gestational diabetes, group 1 for type-1 diabetes (adolescent diabetes), bunch 2 for type-2 diabetes. This grouped dataset was given as contribution to the arrangement model which further orders every understanding's danger levels of diabetes as gentle, moderate, and extreme. Further, execution investigation of different calculations has been done on this information to analyze diabetes. The accomplished outcomes show the presentation of each order calculation.

Classification, Clustering, Data Mining Techniques, Diagnosis of Diabetes, Expert Clinical System, Naive Bayes, Random Tree, C4.5, Simple Logistic.

"Diagnosticate Diabetes using Data Mining Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.1145-1149, December 2018, Available :http://www.jetir.org/papers/JETIR1812D65.pdf

"Diagnosticate Diabetes using Data Mining Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp1145-1149, December 2018, Available at : http://www.jetir.org/papers/JETIR1812D65.pdf