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@article{181045,
        author = {Chhoti Kumari and Anoop Kumar Sahani and Archit Siwach and Ritesh Kumar Tripathi},
        title = {AI - DRIVEN DISEASE DETECTION USING MACHINE LEARNING AND DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {1},
        pages = {3285-3295},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181045},
        abstract = {Increased outbreaks of conditions like 
diabetes and dermatological diseases have made 
effective disease diagnosis systems an essential tool to 
prevent such occurrences early enough. This paper 
outlines the development of a web system for 
predicting disease using machine learning algorithms. 
It offers the client an interface platform where he can 
scan for likely skin conditions based on uploading of 
images and project diabetes through evaluating 
chosen symptoms. It utilizes CNNs for diagnosing 
dermatological conditions and Random Forest to 
predict diabetes risk, showing the potential of AI
based preliminary diagnostics. In addition, the 
system incorporates a friendly interface to improve 
accessibility and usability. It also has an easy-to-use 
dashboard for users to monitor their health status in 
the long term, enabling them to view trends and 
patterns in their conditions. The system can be 
extended to forecast other diseases by adding other 
datasets and machine learning models. Our findings 
prove encouraging accuracy, which indicates the 
potential of disease prediction systems driven by AI in 
supporting early diagnosis, lowering costs of diagnosis, 
and enhancing outcomes for patients.},
        keywords = {Disease Prediction, Machine Learning,  CNN, Random Forest, Diabetes, Skin Diseases, Web  Application, AI- Driven Diagnosis.},
        month = {},
        }