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@article{164663,
        author = {Kamran Rashid Thoker and Deepak Singh and Abhishek},
        title = {INSTAMEDI : HEALTHCARE CONSULTATION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {1518-1520},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164663},
        abstract = {The proliferation of computer-based technology in healthcare has led to a surge in electronic data, posing challenges for medical practitioners in accurately analyzing symptoms and diagnosing diseases early. Supervised machine learning (ML) algorithms offer a promising solution, surpassing standard systems in disease detection. This review aims to identify trends in disease detection across various supervised ML models, including Naïve Bayes, Decision Trees, and K-Nearest Neighbor. Support Vector Machine emerges as adept in detecting kidney and Parkinson's diseases, while Logistic Regression excels in heart disease prediction. Furthermore, Random Forest and Convolutional Neural Networks demonstrate precision in breast disease and common disease prediction, respectively. This analysis underscores the potential of ML in enhancing healthcare diagnostics.},
        keywords = {Health Care, Supervised Machine Learning, Disease Prediction.},
        month = {},
        }