IJSTR >> Volume 9 - Issue 3, March 2020 Edition

[Full Text]

AUTHOR(S)

K.Renuka Devi, J.Bhavithra, Dr.A.Saradha

KEYWORDS

Recommender systems, collaborative filtering, Improved K-means clustering, Improved Krill-Herd Optimization, Diabetes.

ABSTRACT

In the growing world of rapid technology, Recommender system (RS) plays one of the significant roles in making decisions to the appropriate users. To maintain blood glucose in balanced level, there is a need of recommender system to recommend appropriate nutrition to those diabetic patients. An optimization technique plays a significant role in refining the attributes of appropriate algorithm to produce more optimized results to the user. The usage of recommender systems is to analyze the individual patient profiles to recommend the specific nutrition by means of collaborative filtering. The Patient’s profile will get generated by analyzing thirty features for each of them. The Improved Krill Herd based optimization with Improved K-Means (IKH-IKC) system clusters those profiles using improved k-means clustering algorithm. To enhance the accuracy of recommendations, Improved Krill-Herd optimization algorithm has been applied. To validate the IKH-IKC idea, the experiment was carried out with 150 patient profiles. The efficiency of IKH-IKC system was analyzed with different metrics like Precision, Recall, F-measure, Accuracy, Matthews correlation, Fallout rate, Miss rate, Root Mean Squared Error (RMSE). The Experimental evaluation conveys that the IKH-IKC idea generates better clustering and optimized results to the user with low error rate.

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