Diabetes Prediction and Recommendation Model Using Machine Learning Techniques and MapReduce

Ritika Bateja  lowast; Sanjay Kumar Dubey; Ashutosh Kumar Bhatt

doi:10.17485/IJST/v17i26.530

Article

Diabetes Prediction and Recommendation Model Using Machine Learning Techniques and MapReduce

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i26.530

Year: 2024, Volume: 17, Issue: 26, Pages: 2747-2753

Original Article

Diabetes Prediction and Recommendation Model Using Machine Learning Techniques and MapReduce

Ritika Bateja^1∗, Sanjay Kumar Dubey², Ashutosh Kumar Bhatt³

¹ Ph.D. Scholar, Department of Computer Science and Engineering, ASET, Amity University Uttar Pradesh, Noida, India
² Associate Professor, Department of Computer Science and Engineering, ASET, Amity University Uttar Pradesh, Noida, India
³ Associate Professor, School of Computer Sciences and Information Technology, Uttarakhand Open University, Haldwani, Uttarakhand, India

*Corresponding Author
Email: [email protected]

Received Date:02 March 2024, Accepted Date:07 June 2024, Published Date:06 July 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To deliver patient centric healthcare for diabetic patients using a fast and efficient diabetic prediction and recommendation model which will not only help in early diagnoses of disease but also recommend appropriate medicine for controlling it at stage 1. Methods: The Support Vector Machine Classifier is further enhanced with Particle Swarm Optimization (PSO) and used for the prediction of diabetes. Collaborative Filtering is used for drug recommendation, which produces a suitable list of medications that correspond to the diagnoses of diabetes patients. Improved Density-Based Spatial Clustering of Applications with Noise (I-DBSCAN) is proposed to cluster EHR data to get labels based on the symptoms of patients and map reduction is utilized to process the clustered data in parallel for quick recommendations. Findings: The accuracy of the SVM with the PSO model is 99.20%. The performance of I-DBSCAN is also compared with K-Means and regular DBSCAN using the Silhouette Score, Davies Bouldin Score, and the Calinski Harabasz Score. Also, I-DBSCAN was found to give a more accurate score. Novelty: The extensive volume of diabetes-related information stored in electronic health records (EHRs) through continuous monitoring devices poses a growing difficulty for healthcare professionals to effectively navigate and deliver patient-centered care. Machine Learning techniques like classification and recommendations can be utilized to facilitate early disease diagnosis and recommend appropriate medications.

Keywords: Electronic health records (EHRs), Collaborative Filtering (CF), Recommendations, Improved Density Based Spatial Clustering of Applications with Noise (IDBSCAN), SVM classifier

References

Roy D, Dutta M. A systematic review and research perspective on recommender systems. Journal of Big Data. 2022;9:59–60.
Almuammar SA, Noorsaeed AS, Alafif RA, Kamal YF, Daghistani GM. The Use of Internet and Social Media for Health Information and Its Consequences Among the Population in Saudi Arabia. Cureus. 2021;13(9):18338. Available from: https://doi.org/10.7759/cureus.18338
Jia X, Pang Y, Liu LS. Online Health Information Seeking Behavior: A Systematic Review. Healthcare (Basel). 2021;9(12):1740. Available from: https://doi.org/10.3390/healthcare9121740
Kuanr M, Mohapatra P, Piri J. Health Recommender System for Cervical Cancer Prognosis in Women. In: 6th International Conference on Inventive Computation Technologies (ICICT). Coimbatore, India. 2021:673–679.
Princy J, Senith S, Kirubaraj AA, Vijaykumar PO. A personalized food recommender system for women considering nutritional information. Int J Pharmaceut Res. 2021;13(2). Available from: https://doi.org/10.1109/ICICT50816.2021.935678
Almatrooshi F, Alhammadi S, Salloum SA, Akour I, Shaalan K. A recommendation system for diabetes detection and treatment. In: International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI). Sharjah, United Arab Emirates. IEEE Institute of Electrical and Electronics Engineers. p. 1–6.
Yera R, Alzahrani AA, Martínez L, Rodríguez RM. A Systematic Review on Food Recommender Systems for Diabetic Patients. Int J Environ Res Public Health. 2023;20(5):4248. Available from: https://doi.org/10.3390/ijerph20054248
Yang C, Xiao C, Ma F, Glass L, Sun J. SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations. In: In Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021. Montreal, QC, USA. p. 19–27.
Batalha A, Ponciano IC, Chaves G, Felício DC, Britto RR, Silva LPD. Behavior change interventions in patients with type 2 diabetes: a systematic review of the effects on self-management and A1c. J Diabetes Metab Disord. 2021;20(2):1815–1836. Available from: https://doi.org/10.1007/s40200-021-00846-8
Janakiraman B, Saradha A. Personalized Nutrition Recommendation for Diabetic Patients Using Optimization Techniques. Intelligent Automation & Soft Computing. 2020;26(2):269–280. Available from: https://doi.org/10.31209/2019.100000150
Bhat SS, Ansari GA. Predictions of diabetes and diet recommendation system for diabetic patients using machine learning techniques. In: 2021 2nd International Conference for Emerging Technology (INCET). Belagavi, India. .
Gong F, Wang M, Wang H, Wang S, Liu M. SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation. Big Data Res. 2021;23:100174. Available from: https://doi.org/10.1016/j.bdr.2020.100174
DFDC, Kaymak U, PVG, Riel NV. A Markov model for inferring event types on diabetes patients data. Healthc Analytics. 2022;2:100024. Available from: https://doi.org/10.1016/j.health.2022.100024
Tan WY, Gao Q, Oei RW, et al. Diabetes medication recommendation system using patient similarity analytics. Sci Rep. 2022;12:20910. Available from: https://doi.org/10.1038/s41598-022-24494-x
Morales LFG, Valdiviezo-Diaz P, Reátegui R, Barba-Guaman L. Drug Recommendation System for Diabetes Using a Collaborative Filtering and Clustering Approach: Development and Performance Evaluation. J. Med. Internet Res. 2022;24:37233. Available from: https://doi.org/10.2196/37233
Kidwell KM, Roychoudhury S, Wendelberger B. Application of Bayesian methods to accelerate rare disease drug development: scopes and hurdles. Orphanet J Rare Dis. 2022;17(186). Available from: https://doi.org/10.1186/s13023-022-02342-5
Ramani R, Devi KV, Soundar KR. MapReduce-based big data framework using modified artificial neural network classifier for diabetic chronic disease prediction. Soft Comput. 2020;24(21):16335–16380. Available from: https://doi.org/10.1016/j.bdr.2020.100174
Arun G, Marimuthu CN. Diabetes classification using MapReduce-based capsule network. Automatika. 2024;65(1):73–81. Available from: https://doi.org/10.1016/j.bdr.2020.100190
Modak SK, Jha VK. Diabetes prediction model using machine learning techniques. Multimedia Tools and Applications. 2023;p. 1–27. Available from: https://doi.org/10.1016/j.bdr.2020.103456

Copyright

© 2024 Bateja et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)