ICTACT Journal on Soft Computing

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Diet Recommendation for Glycemic Patients using Improved Kmeans and Krill-Herd Optimization


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1 Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, India
     

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Maintaining nutrition for glycemic (diabetic) patients in order to retain the blood glucose level is one of the important activity to be followed. Stimulating the amount of carbohydrates, protein, vitamins, and minerals will result in a healthy diet. So, there is a necessity for recommendation of nutrition to those diabetic patients nowadays. Recommender Systems (RS) play a vital role in urging relevant suggestions to the users. To promote improvised and optimized results, Optimization technique plays a significant role in refining the parameters of chosen algorithm. To optimize and to upgrade the accuracy of recommendations, the system has been developed by implementing improved Krill-Herd algorithm. The system which clusters the profiles of diabetic patients using improved k-means clustering algorithm and results has been optimized using Improved Krill-Herd optimization algorithm. The performance will be analysed using different measures like Precision, Recall, F-measure, Accuracy, Matthews correlation, Fallout rate and Miss rate. The evaluation results show that the proposed system performs better and produces optimized results to the diabetic patients with minimum error rate.

Keywords

Data Mining, Diabetes Patients, Recommender Systems, Clustering Algorithm, Improved K-Means, Krill Herd Optimization.
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  • Y. Koren and R.M. Bell, “Advances in Collaborative Filtering”, Springer, 2011.

  • Kunal Shah, “Unsupervised Recommender Systems: An Overview of Different Approaches to Recommendations”, Proceedings of IEEE International Conference on Innovations in Information, Embedded and Communication Systems, pp. 1-4, 2017.

  • Xiaofeng Li, “Improved Hybrid Collaborative Filtering Algorithm Based on K-Means”, Expert Systems with Applications, Vol. 842, pp. 928-934, 2019.

  • Semeh Ben Salema and Sami Naouali, “A Fast and Effective Partitional Clustering Algorithm for Large Categorical Datasets using a K-means Based Approach”, Computers and Electrical Engineering, Vol. 37, No. 9, pp. 463-483, 2018.

  • J. Hu, J. Liang, Y. Kuang and V. Honavar, “A User Similarity-Based Top-N Recommendation Approach for Mobile In-Application Advertising”, Expert Systems with Applications, Vol. 111, pp. 51-60, 2018.

  • Anand Khandare and A.S. Alvi, “Clustering Algorithms: Experiment and Improvements”, Computing and Network Sustainability, Vol. 25, No. 12, pp. 263-271, 2017.

  • Laith Mohammad Abualigah, Ahamad Tajudin Khader, Essam Said Hanandeh, and Amir H. Gandomi, “A Novel Hybridization Strategy for Krill-Herd Algorithm Applied to Clustering Techniques”, Applied Soft Computing, Vol. 60, pp. 423-435, 2017.

  • Laith Mohammad Abualigah, Ahamad Tajudin Khader and Essam Said Hanandeh, “Hybrid Clustering Analysis using Improved Krill-Herd Algorithm”, Applied Intelligence, Vol. 48, No. 11, pp. 4047-4071, 2018.

  • D. Dua and E. Karra Taniskidou, “UCI Machine Learning Repository”, Available at: http://archive.ics.uci.edu/ml, Accessed on 2017.

  • Methods and Application of Food Composition Laboratory, Available at: http://www.ars.usda.gov/Services/docs.htm?docid=8964.

  • R.V. Rao, D.P. Rai and J. Balic, “A Multi-Objective Algorithm for Optimization of Modern Machining Processes”, Engineering Applications of Artificial Intelligence, Vol. 61, pp. 103-125, 2017.

  • Divya Pandove, “A Comprehensive Study on Clustering Approaches for Big Data Mining”, Proceedings of IEEE International Conference on Electronics and Communication System, pp. 1333-1338, 2015.

  • Jonathan Jeffrey, “Optimizing Web Structures using Web Mining Techniques”, Springer, 2007.

  • Daniel Kluver, M.D. Ekstrand and J.A. Konstan, “Rating-Based Collaborative Filtering: Algorithms and Evaluation”, Social Information Access, Vol. 42, No. 8, pp. 344-390, 2018.

  • Gunnar Schroder, Maik Thiele and Wolfgang Lehner, “Setting Goals and Choosing Metrics for Recommender System Evaluations”, Proceedings of Workshop at 5th ACM Conference on Recommender Systems, pp. 53-61, 2011.

  • M.V.B.T. Santhi, “Enhancing K-Means Clustering Algorithm”, International Journal on Computer Science and Technology, Vol. 2, No. 4, pp. 235-246, 2012.

  • Huanjing Wang and Jaason Van Hulse, “A Comparative Study of Threshold-based Feature Selection Techniques”, Proceedings of IEEE International Conference on Granular Computing, pp. 499-504, 2010.

  • X. Chen, X. Xu, J.Z. Huang and Y. Ye, “K Means: Automated Two-Level Variable Weighting Clustering Algorithm for Multiview Data”, IEEE Transactions on Knowledge and Data Engineering, Vol. 25, No. 4, pp. 932-944, 2013.

  • G.Y. Zhang, C.D. Wang and D. Huang, “K-Means: Two Level Weighted Collaborative K-Means for Multiview Clustering”, Knowledge Based Systems, Vol. 150, pp. 127-138, 2018.

  • X.T. Zhang, “Multi-Task Multi-View Clustering”, IEEE Transactions on Knowledge and Data Engineering, Vol. 28, No. 4, pp. 3324-3338, 2016.

  • L.M. Abualigah, A.T. Khader, E.S. Hanandeh and A.H. Gandomi, “A Novel Hybridization Strategy for Krill-Herd Algorithm Applied to Clustering Techniques”, Applications of Soft Computing, Vol. 60, pp. 423-435, 2017.

  • J. Bhavithra and A. Saradha, “Personalized Web Page Recommendation using Case-Based Clustering and Weighted Association Rule Mining”, Cluster Computing, Vol. 22, No. 3, pp. 6991-7002, 2018.

  • K. Renuka Devi, “Evaluation of Partitional and Hierarchical Clustering Techniques”, International Journal of Computer Science and Mobile Computing, Vol. 8, No. 11, pp. 48-54, 2019.

  • K. Renuka Devi, J. Bhavithra and A. Saradha, “Personalized Nutrition Recommendation for Diabetic Patients using Improved K-Means and Krill-Herd Optimization”, International Journal of Scientific and Technology Research, Vol. 9, No. 3, pp. 1076-1083, 2020.


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  • Diet Recommendation for Glycemic Patients using Improved Kmeans and Krill-Herd Optimization

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Authors

K. Renuka Devi
Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, India
J. Bhavithra
Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, India
A. Saradha
Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, India

Abstract


Maintaining nutrition for glycemic (diabetic) patients in order to retain the blood glucose level is one of the important activity to be followed. Stimulating the amount of carbohydrates, protein, vitamins, and minerals will result in a healthy diet. So, there is a necessity for recommendation of nutrition to those diabetic patients nowadays. Recommender Systems (RS) play a vital role in urging relevant suggestions to the users. To promote improvised and optimized results, Optimization technique plays a significant role in refining the parameters of chosen algorithm. To optimize and to upgrade the accuracy of recommendations, the system has been developed by implementing improved Krill-Herd algorithm. The system which clusters the profiles of diabetic patients using improved k-means clustering algorithm and results has been optimized using Improved Krill-Herd optimization algorithm. The performance will be analysed using different measures like Precision, Recall, F-measure, Accuracy, Matthews correlation, Fallout rate and Miss rate. The evaluation results show that the proposed system performs better and produces optimized results to the diabetic patients with minimum error rate.

Keywords


Data Mining, Diabetes Patients, Recommender Systems, Clustering Algorithm, Improved K-Means, Krill Herd Optimization.

References