Disease Classification with LSTM and Logistic Regression Models using POS Tagging Categories

In the past time, patients able to know illness when seeing a doctor only and patient-doctor communication is an important component of patient care without an intense focus on skills of communication between doctor and patient. But the information on diseases at the present time are published on the internet and almost contents consist of symptoms, causes and prevention and treatment methods including a largely private data of illness transforms into an increasingly public experience. So internet health information is used to improve health literacy in the patient- physician relationship. The research uses the information of diseases from the internet which presents by using Thai language for determining of 24 diseases to develop tool which is the model for helping to health literacy an increasingly essential competency required of today's citizens by using LSTM and Linear Regression algorithms which use word embedding as the Bag of words and POS tagging categories found that both algorithms show high accuracy by using POS tagging in 1, 2 and 4 categories.