A Detailed Review on Disease Prediction Models that uses Machine Learning

Authors

  • Ehtisham Farooqui tudent, Department of Computer Science and Engineering, Integral University, Lucknow, India Author
  • Jameel Ahmad Assistant Professor, Department of Computer Science and Engineering, Integral University, Lucknow, India. Author

Keywords:

Decision Tree, Machine Learning, Naïve Bayes, Random Forest

Abstract

Human body is guarded by the immune  system, but sometimes this immune system alone is not  capable of preventing our body from diseases.  Environmental conditions and living habits of people are  the cause of many diseases that are the main reason for a  huge number of deaths in the world, and diagnosing these  diseases sometimes becomes challenging. We need an  accurate, feasible, reliable, and robust system to diagnose  diseases in time so that these can be properly treated. With  the growth of medical data, many researchers are using  these medical data and some machine learning algorithms  to help the healthcare communities in the diagnosis of  many diseases. In this paper a survey of various models  based on such algorithms, techniques is presented and their  performance is analyzed. Researches have been conducted  on various models of supervised learning algorithms and  some of them are Support Vector Machine (SVM),  K-Nearest Neighbor (KNN), Decision Tree (DT), Naïve  Bayes and Random Forest (RF).

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Published

2020-07-04

Issue

Vol. 8 No. 4 (2020): International Journal of Innovative Research in Computer Science and Technology (Jul) 2020

Section

Articles

How to Cite

A Detailed Review on Disease Prediction Models that uses Machine Learning. (2020). International Journal of Innovative Research in Computer Science & Technology, 8(4), 326–330. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/13244