A Literature Survey of Machine Learning Techniques for Classification and Prediction of Heart Disease

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@article{1703869,
      author = {Bhavika Chandrabhan Gupta, Jitendra Dangra},
      title = {A Literature Survey of Machine Learning Techniques for Classification and Prediction of Heart Disease},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {4},
      pages = {151-155},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703869.pdf},
      abstract = {Data mining strategies have previously been used by a number of investigators in order to locate ailments. It is essential to keep in mind that not all methods of sickness prediction are developed in the same way. There is a possibility that the accuracy of disease prediction may be enhanced. In this article, we provided an overview of the many different methods for the classification of data that are presently being used. These algorithms, in a way, are symbolic representations of themselves. The classification of data is a common activity that requires a lot of processing power. In addition to this, we have established a foundation for classifying data. In the course of this activity, we will be analysing and comparing the most significant algorithms among the multitudes that are accessible nowadays. This study presents a literature survey of Machine learning techniques for classification and prediction of heart disease},
      keywords = {Data Mining, Disease Prediction, Decision Tree, KDD},
      month = {October},
  }