Disease Prediction from Symptom Descriptions Using Deep Learning and NLP Technique

Accurate disease prediction from symptom descriptions is vital for improving early detection and enabling remote healthcare services, especially in the evolving landscape of digital health. Traditional diagnosis methods face significant limitations due to their reliance on structured datasets and subjective assessments, leading to delays and inefficiencies in the diagnosis process. Our strategy is to employ advanced NLP techniques such as tokenization and TF-IDF, along with DL techniques like LSTM, CNN-LSTM, and GRU, to analyze unstructured symptom data and more accurately predict diseases The study also compares two text transformation techniques (TF-IDF vectorization and tokenization) with traditional Machine Learning (ML) methods like Decision Trees to specify the best technique. Through intensive experiments on two datasets (one with 24 diseases and one with 41 diseases), the efficiency of the proposed methods is verified and the importance of using NLP and deep learning in revolutionizing healthcare is illustrated, particularly in upgrading remote diagnosis and enabling early medical intervention. The best-performing model, CNN-LSTM using tokenized text, achieved 99.90% accuracy on a 41-disease dataset, and LSTM with TF-IDF achieved 98.8% accuracy on a 24-disease dataset, outperforming or matching results from more complex models in prior studies. The findings show that combining NLP and deep learning enables accurate, efficient disease prediction, advancing remote care and early intervention in digital healthcare.

@article{Al-qarni2025,
  title     = {Disease Prediction from Symptom Descriptions Using Deep Learning and NLP Technique},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Salmah Saad Al-qarni and Abdulmohsen Algarni},
  doi       = {10.14569/IJACSA.2025.0160541},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160541}
}