Processing With Patients’ Statements

Abstract

This paper represents a novel strategy for developing a disease diagnosis gadget from a patient’s statement. For that, the system solely accepts patients’ statements in a natural language like English and analyzes the patients’ statements to prognosis the symptoms the affected person is presently suffering from. The framework forms the patients’ discourse and afterward utilizes Term Frequency (TF) to find the indications of a malady. Cosine Similarity is utilized to settle on a final decision with respect to regarding disease diagnosis task. Cosine Similarity quantifies the similitude between two non-zero vectors in a vector space model where one of the vectors is constructed with the symptoms the patient is encountering and the rest is developed during knowledge base setup. The framework is tested over 1013 patients with various ailments and its accuracy up to 98.3%.