Disease Prediction Using Machine Learning Over Big Data

Authors

  • Ajeesh Babu Dept. of Computer Science and Engineering, St Thomas College of Engineering and Technology, APJ Abdul Kalam
  • Fathima Basheer Dept. of Computer Science and Engineering, St Thomas College of Engineering and Technology, APJ Abdul Kalam
  • Jayasanker M Dept. of Computer Science and Engineering, St Thomas College of Engineering and Technology, APJ Abdul Kalam
  • Tintu Mariyam Paul Dept. of Computer Science and Engineering, St Thomas College of Engineering and Technology, APJ Abdul Kalam
  • Sithu Ubaid Dept. of Computer Science and Engineering, St Thomas College of Engineering and Technology, APJ Abdul Kalam

DOI:

https://doi.org/10.26438/ijcse/v8i7.1115

Keywords:

Big Data, Machine Learning, kaggle, CNN

Abstract

Due to big data and progress in biomedical and healthcare communities, accurate study of medical data benefits early disease recognition, patient care and community services. When the quality of medical data is incomplete, the exactness of study is reduced. In the proposed system, our system can take either text or image input symptoms from the user and based on the analysis of the symptoms it displays a result. It provides machine learning algorithms for effective prediction of various disease occurrences in disease-frequent societies. It experiment the altered estimate models over reallife hospital data collected. To overcome the difficulty of incomplete data, it uses a latent factor model to rebuild the missing data. It experiments on various diseases that occur in human being. Using structured and unstructured data from hospital, Random Forest algorithm is used for classification of text datasets. SSD (Single Shot Multi Box Detector) algorithm is used for image processing to analyse various diseases in human being.

References

[1] M. Chen, Y .Hao, K. Hwang, L. Wang, and L. Wang, "Disease prediction by machine learning over big data from healthcare communities", ,"IEEE Access, vol.5, no. 1, pp. 8869-8879, 2017.

[2] IM. Chen, Y. Ma, Y. Li, D. Wu, Y. Zhang, and C. Youn,"Wearable 2.0: Enable human-cloud integration in next generation healthcare system," IEEE Commun. , vol. 55, no. 1,pp. 54"61, Jan. 2017.

[3] P. Sharma, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, V. Yadav, S. Chauhan, Analytical comparison of efficacy for electromyography and galvanic skin resistance biofeedback on audio-visual mode for chronic TTH on various attributes, in Proceedings of the ICCIDA-2018 on 27 and 28th October 2018. CCIS Series (Springer, Gandhi Institute for Technology, Khordha, Bhubaneswar, Odisha, India, 2018)

[4] Shraddha SubhashShirsath "Disease Prediction Using Machine Learning Over Big Data" International Journal of Innovative Research in Science, Vol. 7, Issue 6, June 2018

[5] AnimeshHazra, Arkomita Mukherjee, Amit Gupta, Mukherjee, "Heart Disease Diagnosis and Prediction Using Machine Learning and Data Mining Techniques: A Review", Research Gate Publications, July 2017, pp.2137-2159.

[6] V. Krishnaiah, G. Narsimha, N. Subhash Chandra, "Heart Disease Prediction System using Data Mining Techniques and Intelligent Fuzzy Approach: A Review", International Journal of Computer Applications, February 2016

[7] AnimeshHazra, Arkomita Mukherjee, Amit Gupta, Asmita Mukherjee, "Heart Disease Diagnosis and Prediction Using Machine Learning and Data Mining Techniques: A Review", Research Gate Publications, July 2017, pp.2137-2159

[8] P. Groves, B. Kayyali, D. Knott, and S. V. Kuiken, "The "big data" revolution in healthcare: Accelerating value and innovation," 2016.

[9] S. Patel and H. Patel, "Survey of data mining techniques used in healthcare domain," Int. J. of Inform. Sci. and Tech., Vol. 6, pp. 53-60, March 2016.

[10] M. Amiri and G. Armano, "Early diagnosis of heart disease using classification and regression trees," The 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, 2013, pp. 1-4. doi: 10.1109/IJCNN.2013.6707080 .

[11] S. Ekız and P. Erdoğmuş, "Comparative study of heart disease classification," 2017 Electric Electronics, Computer Science, Biomedical Engineerings` Meeting (EBBT), Istanbul, 2017, pp. 1-4. doi: 10.1109/EBBT.2017.7956761.

[12] P. Su, J. Yang, Z. Li and Y. Liu, "Mining Actionable Behavioral Rules Based on Decision Tree Classifier," 2017 13th International Conference on Semantics, Knowledge and Grids (SKG), Beijing, 2017, pp. 139-143. doi: 10.1109/SKG.2017.00030 .

[13] D. Bertsimas, J. Dunn and A. Paschalidis, "Regression and classification using optimal decision trees," 2017 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, 2017, pp. 1-4. doi: 10.1109/URTC.2017.8284195Y

Downloads

Published

2020-07-31
CITATION
DOI: 10.26438/ijcse/v8i7.1115
Published: 2020-07-31

How to Cite

[1]
A. Babu, F. Basheer, J. M, T. M. Paul, and S. Ubaid, “Disease Prediction Using Machine Learning Over Big Data”, Int. J. Comp. Sci. Eng., vol. 8, no. 7, pp. 11–15, Jul. 2020.

Issue

Vol. 8 No. 7 (2020): IJCSE July Edition

Section

Research Article

License

Creative Commons License

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

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.