Improving The Accuracy of Cardiotocogram Machine Analysis Using Artificial Neural Network (ANN)

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@article{1703294,
      author = {Okeke Amaka Josephine, Prof. James Eke, Emetu Chukwuma Kalu},
      title = {Improving The Accuracy of Cardiotocogram Machine Analysis Using Artificial Neural Network (ANN)},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {9},
      pages = {409-421},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703294.pdf},
      abstract = {This work aims at improving the Accuracy of Cardiotocogram Machine Analysis using Artificial Neural Network (ANN). There is a need to improve the cardiotocogram machine analysis because the conventional method of analysis has a major drawback of impression and inaccuracy. This drawback, if not properly analyzed will lead to permanent fetal brain damage or death. From the literatures reviewed, it was discovered that there is a need to develop a reliable technique that will reduce the incidence of unnecessary medical intervention and fetal injury during child labor. To achieve this, artificial neural network algorithm for cardiotocogram analysis was developed. A sample of five patients were collected from the existing result of the cardiotocogram from ESUT Teaching Hospital and it was saved in Microsoft excel. The result was injected to MATLAB 2015a, where the data was trained. The result achieved a classification accuracy of 98.34% which is very good. The system was compared with the other state of the art algorithms and the result showed 2.11% better then the best existing system performance.},
      keywords = {Improving, Cardiotocogram, Machine analysis, and Artificial neural network},
      month = {March},
  }