Authors : M V V Krishna; G Sri Jaya Sairam; P Karthik; M Shakeer; G Arjun; SD Basheer Babu

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/ybxakyu6

Scribd : https://tinyurl.com/2vjx9npm

DOI : https://doi.org/10.38124/ijisrt/25apr760

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Abstract : The project "Gen AI for Disease Prediction", utilizes advanced machine learning methodologies to forecast diseases such as diabetes, heart disease, and cancer based on user-input symptoms. It employs the Random Forest algorithm, a powerful and flexible machine learning model, ensuring accurate predictions while reducing the likelihood of overfitting. To enhance prediction reliability, the system incorporates data preprocessing techniques such as feature selection, data cleaning, and encoding. Developed using Scikit-learn, Python, and Django, the project integrates sophisticated machine learning functions with an intuitive web interface. Users can conveniently select symptoms from dropdown menus, which are then processed by the backend system. The machine learning model, trained on a well-structured dataset covering various medical conditions and their symptoms, analyzes the input to generate predictions. Ultimately, this project delivers a scalable and efficient disease prediction system that aids in the early detection of potential health issues.

Keywords : Random Forest Algorithm, Medical Diagnosis, Scikit-Learn, Symptom Analysis, Early Disease Detection.

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