Predicting the Side Effects of Medical Drugs Using Sentimental and Classification Mining Algorithms: A Case Study of Hydroxychloroquine and Azithromycin Drugs

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@article{1704153,
      author = {Ali-Okoro Sochima Godson, Olebara Comfort Chinaza, Oguoma Ikechukwu Stanley},
      title = {Predicting the Side Effects of Medical Drugs Using Sentimental and Classification Mining Algorithms: A Case Study of Hydroxychloroquine and Azithromycin Drugs},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {9},
      pages = {153-164},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704153.pdf},
      abstract = {Identification of a problem before it gets worst is a very important aspect of protection more especially when it involves human lives. Drugs are taken for two main reasons, either for cure or for prevention. The motivation behind the study is because of high increase of complains received from most hospitals and clinics within Nigerian medical field, some drug side effects are so disturbing that its results causes abnormal disorder on patients when used for medical treatment more precisely when used on pregnant women. More so, the lack of dataset on most drugs from peoples view or opinion on the drugs which could make it easier for academic and scientific research to be conducted. Furthermore, this study is aimed at predicting the side effects of Hydroxychloroquine and Azithromycin drugs using sentimental and classification data mining algorithms. The study also provides an online platform which could enable medical professionals and the general public to have access to provide report or feedback on the consumed drugs. The online platform was developed with HTML,CSS, PHP and MYSQL while the dataset was sourced from UCI machine learning repository and analyzed using R language and RStudio.  SEMMA which stands for Sample Explore Modify Model Access was applied as the methodology while aspect-base sentimental analysis and Classification decision tree Model algorithms was adopted. The result after the experiment created a model that was able to predict patient?s opinion and reviews on consumption of the drugs base on their various dosage, age and number of patients while the online interface of the drug effects feedback reporting was developed and deployed in the cloud for fast and easy access for users remotely.},
      keywords = {Machine Learning, Drug Effects, Classification Model, Sentimental analysis, Data mining and online Drug Effect Platform},
      month = {March},
  }