Hybrid Deep Learning-Based Artificial Intelligence Framework For Early Cancer Detection And Preventive E-Healthcare Systems

Cancer will continue to be a leading cause of mortality worldwide, making early detection and timely intervention essential for improving survival rates. This study will propose a hybrid Artificial Intelligence (AI)-based healthcare framework for early cancer detection and preventive analysis using deep learning techniques. The model will integrate Convolutional Neural Networks (CNN) for medical image feature extraction and Long Short-Term Memory (LSTM) networks for analyzing sequential clinical data.The system will be evaluated on benchmark cancer datasets using performance metrics such as accuracy, precision, recall, and F1-score. The proposed hybrid model is expected to outperform traditional machine learning approaches by achieving higher accuracy and lower error rates.The framework will support early-stage diagnosis, risk prediction, and personalized preventive strategies. Although challenges such as computational complexity and data privacy will persist, the proposed system is anticipated to offer strong potential for real-world healthcare applications and contribute to AI-driven cancer care.