A latent semantic analysis method for ranking the results of human disease search engine

The human disease search engine based on the query about factors (symptom, cause, position happening symptoms, i.e.,) helps users to conveniently diagnose they may have anytime, anywhere. Therefore, results returned by need be accurate and ranked reasonably so that can know which has highest probability for their query. We propose a method arrange diseases latent semantic analysis (LSA) technique. This rank meaningfully because it not only exploits matching term frequency-inverse document frequency (TF-IDF) scores between in results, but also implicit relationship results.