• Randheer singh
• Dr. Reema Ajmera
• Mr. Deevesh Choudhary

The inexhaustible measure of information covered up in clinical databases that can be proficiently use to finding of patient's infections. This shrouded data exhibit in clinical databases can be utilized as rules after long procedure of lab examine, master check and positive outcome with understanding infections. This research work implement a hybrid clustering with classification model comprising of enhanced k-means and Logistic Regression classifier. The proposed technique comprises of clustering with classification i.e. clustered data is given to the classification for the evaluating the mining patterns. This research work also analyses the existing computational intelligence techniques for predicting diabetes. Experimental results demonstrate that the proposed technique outperforms the existing techniques.

diabetes, clinical databases, clustering, classification, k-means, logistic regression, data mining, prediction

"Prediction analysis for Diabetic patients using Clustered based Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.770-776, July-2018, Available :http://www.jetir.org/papers/JETIR1807469.pdf

"Prediction analysis for Diabetic patients using Clustered based Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp770-776, July-2018, Available at : http://www.jetir.org/papers/JETIR1807469.pdf