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@article{190818,
        author = {Prof. Vitthal M. Patil and Dr. Manoj S. Sonawane},
        title = {COVID-19 Prediction Model With K-Nearest Neighbor Algorithm in India},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {6383-6386},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190818},
        abstract = {This study proposes a COVID-19 prediction model based on symptoms using the k-nearest neighbor (KNN) algorithm. The model's effectiveness was evaluated through an experiment, and the results were analyzed to assess its predictive accuracy. The study utilized COVID-19 prediction data from the GitHub machine learning data repository, which includes 2575 patients from India, who were either positive or negative for COVID-19. These patients exhibited symptoms such as fever, body aches, runny nose, and dyspnea, with infection probabilities labeled as 1 (positive) or 0 (negative). The dataset encompasses patients ranging in age from 1 to 100, with varying fever levels. Breath issues were categorized into three types: mild (0), severe (1), and none (-1), while body soreness and runny nose symptoms were classified into two distinct categories. The data was primarily gathered through self-collection methods. After analyzing the experimental results, the KNN model demonstrated a predictive accuracy of 98.36%.},
        keywords = {COVID-19, KNN Model, Dataset, Supervised Machine Learning, Disease prediction.},
        month = {January},
        }