Lifestyle Disease Prediction

Authors

  • P. Sumalatha  Assistant Professor, Department of CSE, Bhoj Reddy Engineering College for Women, Hyderabad, India
  • M Akanksha  Department of CSE, Bhoj Reddy Engineering College for Women, Hyderabad, India
  • Maria Fatima  Department of CSE, Bhoj Reddy Engineering College for Women, Hyderabad, India

Keywords:

Deoxyribonucleic acid testing, healthcare industries, lifestyle diseases, support vector machine

Abstract

Conditions that are associated with the way a person or group of people live are known as life conditions. Healthcare assiduity collects enormous complaint- related data that's unfortunately not booby-trapped to discover retired information that could be used for effective decision timber. This study aims to understand support vector machine and use it to prognosticate life conditions that an individual might be susceptible to. also, we propose and pretend an profitable machine literacy model as an volition to deoxyribonucleic acid testing that analyzes an existent’s life to identify possible pitfalls that form the foundation of individual tests and complaint forestallment, which may arise due to unhealthy diets and inordinate energy input, physical dormancy, etc. The simulated model will prove to be an intelligent low- cost volition to descry possible inheritable diseases caused by unhealthy cultures.

References

  1. D. W. Bates, S. Saria, L. Ohno-Machado, A. Shah, and G. Escobar, “Big data in health care: using analytics to identify and manage high-risk and high-cost patients,” Health Affairs, vol. 33, no. 7, pp. 1123–1131, 2014.
  2. K.R.Lakshmi, Y.Nagesh and M.Veera Krishna," Performance comparison of three data mining techniques for predicting kidney disease survivability”, International Journal of Advances in Engineering & Technology, Mar. 2014.
  3. Mr. Chala Beyene, Prof. Pooja Kamat, “Survey on Prediction and Analysis the Occurrence of Heart Disease Using Data Mining Techniques”, International Journal of Pure and Applied Mathematics, 2018.
  4. Boshra Brahmi, Mirsaeid Hosseini Shirvani, “Prediction and Diagnosis of Heart Disease by Data Mining Techniques”, Journals of Multidisciplinary Engineering Science and Technology, vol.2,pp.164-168,2 February 2015,.
  5. A. Singh, G. Nadkarni, O. Gottesman, S. B. Ellis, E. P. Bottinger, and J. V. Guttag, “Incorporating temporal her data in predictive models for risk stratification of renal function deterioration,” Journal of biomedical informatics, vol. 53, pp. 220–228, 2015.
  6. 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.
  7. M. Patil, V. B. Lobo, P. Puranik, A. Pawaskar, A. Pai, and R. Mishra,“A proposed model for lifestyle disease prediction using support vector machine,” in 2018 9th International Conference on Computing, Com-munication and Networking Technologies (ICCCNT),pp. 1–6,2018.
  8. F. Q. Yuan, “Critical issues of applying machine learning to condition monitoring for failure diagnosis,” in 2016 IEEE International Conference on Industrial Engineering and Engineering Management(IEEM),2016,pp. 1903– 1907.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2023-06-30

Issue

Volume 10, Issue 3, May-June-2023

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

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

How to Cite

[1]
P. Sumalatha, M Akanksha, Maria Fatima "Lifestyle Disease Prediction" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 3, pp.39-42, May-June-2023.