Review on Different Algorithms for Disease Prediction

@article{1704283,
      author = {Om Khedkar, Rutuja Labhshetwar, Jayant Khandebharad, Vaishnavi Dhakare, Prof. Parag Jambhulkar},
      title = {Review on Different Algorithms for Disease Prediction},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {10},
      pages = {475-478},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704283.pdf},
      abstract = {Any health-related concern must be accurately and promptly examined to prevent and treat illness. The traditional diagnostic approach might not be sufficient in the case of a serious illness. A diagnosis that is made using machine learning (ML) can be more accurate than one made using conventional methods in the construction of a medical diagnosis system for disease prediction. Supervised machine learning (ML) algorithms have shown tremendous potential in outperforming traditional systems for illness diagnosis, aiding medical personnel in the early detection of high-risk disorders.This study discusses a number of algorithms that can be employed to identify diseases based on the patient's current symptoms.We also provide a summary of the outcomes produced by the various algorithms.},
      keywords = {Machine learning, Decision Tree Classifier, Random Forest Classifier, Na?ve Bayes Classifier, Support Vector Machine, K-Nearest Neighbors, ID3},
      month = {April},
  }