A Comparative Analysis of Machine-Learning Algorithms to Build a Predictive Model for Diabetes Disease

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@article{1702728,
      author = {Olatunde Olukemi Victoria, Adesomoju Fisayo Adeyemo},
      title = {A Comparative Analysis of Machine-Learning Algorithms to Build a Predictive Model for Diabetes Disease},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {4},
      number = {11},
      pages = {231-239},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1702728.pdf},
      abstract = {Machine Learning is concerned with the development of algorithms and techniques that allows the computers to learn and gain intelligence based on the past experience. It is a branch of Artificial Intelligence (AI) closely related to statistics. By learning it means that the system is able to identify and understand the input data, so that it can make decisions and predictions based on the data. In this paper, a web based comparative analysis of various machine learning algorithms (Decision Tree, Support Vector Machine, K-Nearest Neighbor, Logistic Regression) was design in order to recognize accurate model for detecting diabetes disease.},
      keywords = {Machine Learning, Diabetes diseases, Decision Tree, Support Vector Machine, K-Nearest Neighbor, Logistic Regression.},
      month = {May},
      doi = {https://doi.org/10.64388/IREV4I11-1702728}
  }