Information Retrieval for Early Detection of Disease Using Semantic Similarity

Abstract

The growth of medical records continues to increase and needs to be used to improve doctors' performance in diagnosing a disease. A retrieval method returns proposed information to provide diagnostic recommendations based on symptoms from medical record datasets by applying the TF-IDF and cosine similarity methods. The challenge in this study was that the symptoms in the medical record dataset were dirty data obtained from patients who were not familiar with biological terms. Therefore, the symptoms were matched in the medical record data with the symptom terms used in the system and from the results, data augmentation was carried out to increase the amount of data up to about 3 times more. In the TF-IDF the highest accuracy with is only , while after augmentation of the test data, the accuracy becomes . The highest accuracy results with the same value using the cosine similarity method is and with the augmented test data accuracy increasing to . From this study it was concluded that a system with sufficient and relevant input of symptoms would provide a more accurate disease prediction. Prediction results using the TF-IDF method with are more accurate than predictions using the cosine similarity method.