Abstract
Biomedical engineering field is one of the most important research areas for patient diagnosis and prediction of diseases using old history of various patient information. Data collection and data analysis models changed business trends over the past few years. By using BigData analytics, we can predict the effects of drugs and how drugs develop disease on mankind. Many machine learning algorithms like cluster computing environment, classification, etc., analyze the content of healthcare. The proposed framework has developed C-means clustering algorithm in developing biomedical engineering applications. The data is collected from machine learning repository. BigData framework MongoDB database is used to analyze the data. Here, we have modules like doctor, administrator, and analyst. By using machine learning and BigData, the analyst module identifies the chronic disease and help in reducing the medical expenses. Doctor by observing the predicted data symptoms predicts related disease. Administrator’s role is to add or remove the users in the database. The researcher finds the patient parameter by using fixed, alphanumerical, variable field data, or combination data which design the exact query which executed on MongoDB server and shows the search result that result has processed by using modified FCM cluster, and finally, we have calculated the accuracy on the basis of search parameter which always helps the researcher for diagnosis of the patient history.
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References
Sajid I, Khan UG, Saba T, Rehman A (2018) Computer-assisted brain tumor type discrimination using magnetic resonance imaging features. Biomed Eng Lett
Chen M, Hao Y, Hwang K, Wang L, Wang L (2017) Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5:8869–8879
Tomasev N, Radovanovi M (2016) Clustering evaluation in high-dimensional data in unsupervised learning algorithms. Springer, Cham, pp 71–107
Auffray C, Balling R, Barroso I et al (2016) Making sense of big data in health research towards an education plan. Genome Med 8(1):71
Lupton D, Jutel A (2015) A critical analysis of self-diagnosis smart-phone apps . Soc Sci Med 133:128–135
Mao R, Xu H, Wu W, Li J, Li Y, Lu M (2015) Overcoming the challenge of variety: big data abstraction. The next evolution of data management for all communication systems. IEEE Commun Mag 53(1):42–47
Costa FF (2014) Big data in biomedicine . Drug Disc Today 19(4):433–440
Claeys OF, Dupont M, Kerckhove T, Verhoeve W, Dhaene P, Turck D (2013) A probabilistic ontology-based plat form for self-learning context-aware healthcare applications. Expert Syst 40:7629–7646
Pedregosa F et al (2011) Scikit-learn: machine learning in python . J Mach Learn Res 12:2825–2830
Mishra NK, Celebi ME (2016) An overview of melanoma detection in dermoscopy images using image processing and machine learning. arXiv: https://arXiv.com/1601.07843
Houle ME, Kriegel HP, Kroge P (2010) Can shared-neighbor distances defeat the curse of dimensionality. In: Proceedings of SSDBM, pp 482–500
Eskofier BM, Lee SI, Daneault JF et al (2016) Recent machine learning advancements in sensor-based mobility analysis. In: IEEE 38th annual international conference of the deep learning for Parkinson’s disease assessment in Engineering in Medicine and Biology Society (EMBC). IEEE, pp 655–658
Yao Q, Tian Y, Li PF, Tian LL, Qian YM, Li JS (2015) Design and development of a medical big data processing system based on hadoop. J Med Syst 39(3):23
Mishu MM (2019) A Patient oriented framework using big data & C-means clustering for biomedical engineering applications. In: 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST). Dhaka, Bangladesh, pp 113–115. https://doi.org/10.1109/ICREST.2019.8644276
Future Work
In the proposed framework, the work has been done on C-means clustering algorithm. So, selecting C-means made the programming part simple. In future, this above-said framework will be developing healthcare data solutions, healthcare intelligence, data accuracy, etc.
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Barapatre, H., Sharma, Y.K., Sarode, J., Shinde, V. (2021). Researcher Framework Using MongoDB and FCM Clustering for Prediction of the Future of Patients from EHR. In: Balas, V.E., Semwal, V.B., Khandare, A., Patil, M. (eds) Intelligent Computing and Networking. Lecture Notes in Networks and Systems, vol 146. Springer, Singapore. https://doi.org/10.1007/978-981-15-7421-4_13
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DOI: https://doi.org/10.1007/978-981-15-7421-4_13
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