Prediction Of Multiple Diseases in Rural Populations Using Machine Learning Techniques Through an Accessible Web-Based Application

@article{1705382,
      author = {Siddharth Pandit, Nitu Sikchi, Tamkeen Sumaiya, Apeksha Ravi, Aasma Verma; Dr. S. Senthilkumar},
      title = {Prediction Of Multiple Diseases in Rural Populations Using Machine Learning Techniques Through an Accessible Web-Based Application},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {7},
      pages = {209-217},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705382.pdf},
      abstract = {This paper provides an in-depth examination of the transformative impact of machine learning algorithms in healthcare, specifically in predictive disease identification. The introduction highlights the revolutionizing role of machine learning in healthcare, enabling systems to learn from medical data for disease prediction without explicit programming. Support Vector Machine, K-Nearest Neighbours, Logistic Regression, Decision Tree, Random Forest, Na?ve Bayes, XGBoost, and AdaBoost are supervised learning algorithms that are investigated for their capacity to examine intricate relationships within datasets and help physicians make well-informed decisions. Then the model is deployed to a web application which is developed using the Django framework.},
      keywords = {Machine learning, healthcare, predictive disease identification, supervised learning algorithms, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, Na?ve Bayes, XGBoost, AdaBoost, medical data, web application, Django framework.},
      month = {January},
  }