Prediction Of Lymphocytosis Using Machine Learning Algorithm Based On Checkup Data

As more and more patients' checkup data are available in the hospital, such large data set contains lots of diagnosis information and can be used to predict specific diseases. In this paper, we use the machine learning method to train the model of the decision tree. Then the patient's physical examination data are used to extract the characteristics and the diagnosis information as the labels to train a prediction model. The model can predict whether the patient has lymphocytosis from the patient's physical examination information. The experimental results show that the accuracy rate is 98.2%, which demonstrates the proposed method can be applied well in predicting other diseases.