Disease diagnosis is difficult in the modern world since hospital visits are sometimes expensive and time-consuming, especially for people who live far from medical facilities. The Disease Predictor provides a practical and affordable solution by estimating the likelihood of a disease based on user-input symptoms using deep learning and symptom analysis. With an emphasis on deep learning methods such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs), this paper investigates predictive modeling for the prediction of human diseases. The suggested methodology improves the efficiency and accuracy of diagnosis by assessing symptom-based inputs. By filling a research gap in the integration of multimodal data for better prediction, this study advances automated, scalable healthcare systems that put patient accessibility and early diagnosis first.

The Conclusions section should clearly explain the main findings and implications of the work, highlighting its importance and relevance.

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