Lung Cancer Prediction and Recommendations Using Machine Learning and Generative AI

This project presents an AI-driven web application designed to predict lung cancer risk and provide person- alized health recommendations. Utilizing a dataset with multiple patient health indicators, the system employs data preprocessing techniques such as categorical encoding and feature scaling. To address class imbalance, Synthetic Minority Oversampling Technique (SMOTE) is applied. The predictive model is built using a Random Forest Classifier trained on the balanced and scaled data, achiev- ing reliable performance for lung cancer detection. The application is implemented with Streamlit, enabling users to input health parameters and receive instant diagnostic results. Additionally, Google Gemini’s generative AI model is integrated to generate concise, doctor-like explanations, outlining causes, symptoms, lifestyle risks, and tailored recommendations based on the prediction. This approach demonstrates the potential of combining machine learning and generative AI to enhance early lung cancer detection and improve preventive healthcare outcomes.