@article{3188,
  author = {Youssef Elmir, Meriem Bendida},
  title = {A Disease Identification System using Electronic Medical Records},
  journal = {Journal of Intelligent Computing},
  year = {2021},
  volume = {12},
  number = {1},
  doi = {https://doi.org/10.6025/jic/2021/12/1/8-24},
  url = {https://www.dline.info/jic/fulltext/v12n1/jicv12n1_2.pdf},
  abstract = {In the medical field, medical analyses are important to properly diagnose the patient’s case by the doctor, especially if there is a history of several analyses of the same patient which are stored in the patient’s electronic medical record; this can help the doctor to make the right decision. However, the doctor always needs other techniques and methods in order to make the right decision. In this work, a disease identification system is performed from electronic
medical records using the k-nearest neighbours classification algorithm, which classifies different types of diseases (six diseases were studied) according to the values of the medical analysis. The experiment results show that the identification rate (classification) is 43.18% using very small sample as reference data, and this obtained result is acceptable and
present the proof of the feasibility of the proposed system.},
}