Overcrowding in receiving patients, medical examinations, and treatment for hospital admission is common at most hospitals in Vietnam. Receiving and classifying patients is the first step in a medical facility’s medical examination and treatment process. Therefore, over- crowding at the regular admission stage has become a complex problem to solve. This work proposes a patient classification scheme representing the text to speed up the patient input flow in hospital admission. First, the Bag of words approach has been built to represent the text as a vector exhibiting the frequency of words in the text. The data used for the eval- uation were collected from March 2016 to March 2021 at My Tho City Medical Center - Tien Giang - Vietnam, including 230,479 clinic symp- tom samples from admissions and discharge office, outpatient depart- ment, accident, and Emergency Department. Among learning approaches used in the paper, Logistic Regression reached an accuracy of 79.1% for stratifying patients into ten common diseases in Vietnam. Besides, we have deployed a model explanation technique, Locally Interpretable Model-Agnostic Explanations (LIME), to provide valuable features in disease classification tasks. The experimental results are expected to sug- gest and classify the patient flow automatically in the hospital admission stage and discharge office to perform the patient flow in the clinics at the hospitals.