Predicting Hepatitis Using Decision Tree and Other Learning Algorithm

@article{1703131,
      author = {Yakubani Yakubu, Augustine S. Nsang},
      title = {Predicting Hepatitis Using Decision Tree and Other Learning Algorithm},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {7},
      pages = {293-302},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703131.pdf},
      abstract = {Hepatitis is a life-threatening disease caused by the swelling of the liver. It can be caused by an infection of the liver by hepatitis viruses (hepatitis B, hepatitis C, and viral hepatitis). Manual examination can be burdensome for large-scale diagnoses leading to severe economic impact to the individual health program. Automated hepatitis prediction using machine learning techniques such as decision tree, kNN and the perceptron offers the promise of serving an effective diagnostic aid. In this study a decision tree is constructed to predict hepatitis, and two other algorithms, kNN and the perceptron, are implemented to predict the same disease.  The three learning algorithms are compared with each other by the extent to which they predict hepatitis. The comparisons are made using the RAND index and confusion matrices.},
      keywords = {Data mining, Decision Tree, Hepatitis, KNN, Perceptron.},
      month = {January},
  }