Ailment Analysis Using Supervised Learning

Authors

  • Mansi Gupta Student, Department of Information Technology, Galgotias College of Engineering and Technology, Greater Noida, India
  • Mayank Dubey Student, Department of Information Technology, Galgotias College of Engineering and Technology, Greater Noida, India
  • Himanshu Yadav Student, Department of Information Technology, Galgotias College of Engineering and Technology, Greater Noida, India
  • Javed Miya Professor, Department of Information Technology, Galgotias College of Engineering and Technology, Greater Noida, India

Keywords:

Supervised learning, Decision Tree, Random Forest, Naïve Bayes algorithm

Abstract

Ailment analysis is built as a generic platform to solve the problem of prediction and analysis of Disease. It is a centralized application that can be used by citizens to keep themselves aware of the disease and casualties if any. To solve these problems, it sees the structured and unstructured data in healthcare field to assess the risk of disease. Disease can be predicted anytime. The system uses Decision tree map algorithm, Random Forest Algorithm, and Naïve Bayes algorithm to generate the pattern and causes of disease. It clearly shows the diseases and sub diseases.

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Published

03-07-2022

Issue

Vol. 5 No. 6 (2022)

Section

Articles

License

Copyright (c) 2022 Mansi Gupta, Mayank Dubey, Himanshu Yadav, Javed Miya

Creative Commons License

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

How to Cite

[1]
M. Gupta, M. Dubey, H. Yadav, and J. Miya, “Ailment Analysis Using Supervised Learning”, IJRESM, vol. 5, no. 6, pp. 322–326, Jul. 2022, Accessed: Dec. 21, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/2232