Study on The Improved Lasso Regression Model for Predicting Medical Expenses

With the improvement of medical consumption level, patients have more and more demand for the prediction of treatment costs. However, the prediction accuracy of existing research methods is low when the amount of data is small. In order to solve this problem, a weighted lasso regression method is proposed to predict the treatment cost based on the electronic medical record. Firstly, a set of transformation method of text-based medical record data is established, and the missing values are supplemented according to the clustering distance to realize the data representation of medical records. Then, in view of the low prediction accuracy of the traditional regression model, the lasso regression model with local weighting is established by introducing the data feature weight into lasso regression method. Finally, the model is verified by the medical record data provided by the hospital, and the results show that the model has higher prediction accuracy.