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@article{169978,
        author = {Sakshi Raju Kaurase and Sayukta Sudhir Giradkar and Sanchi Bharat Borkar and Bhumika Sanjay Rasekar and Prof.Rupatai Lichode},
        title = {Toxic Comments Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2863-2868},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169978},
        abstract = {The widespread adoption of computer-based technology in the healthcare industry has resulted in a substantial increase in electronic data. This large amount of information poses challenges for healthcare professionals in effectively analysing symptoms and detecting diseases at an early stage. However, supervised machine learning (ML) algorithms have shown significant potential in surpassing conventional diagnostic methods and aiding healthcare providers in the timely identification of high-risk conditions. This literature review seeks to explore trends among various supervised ML models employed in disease detection by evaluating their performance metrics. The most commonly referenced supervised ML algorithms include Decision Trees (DT), Support Vector Machines (SVM), and Random Forest (RF), all of which have demonstrated high accuracy in predicting prevalent diseases. Supervised machine learning algorithms have emerged as a leading approach in the field of data mining. Recently, the application of these methods in disease prediction utilizing health data has demonstrated significant potential. This research seeks to explore the primary trends associated with various supervised machine learning algorithms, focusing on their effectiveness and application in predicting disease risk [1].},
        keywords = {Machine Learning, Disease Prediction, SVM, Random Forest, Decision Tree, Classification.},
        month = {November},
        }