Prediction of hospital length of stay using data analytics | |
| Author | Aryal, Alok |
| Call Number | AIT Thesis no.IM-21-04 |
| Subject(s) | Length of Stay--Data processing Hospital utilization--Length of stay--Data processing Machine learning |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Information Management |
| Publisher | Asian Institute of Technology |
| Abstract | Length of Stay is one of the factors that influence the hospital performance as well as it helps hospital organization for better resource utilization with anticipating the demand as well as insurance companies can know about the patients stay if they claim for it. Also predicting total expenses of patient stay will benefit patients to plan beforehand as well. The overall objective of this study is to conduct an analysis and develop a more accurate model for multiple diseases and with multiple hospitals to predict length of stay and also total expenses for the stay of the patient. The use of regression models is done to compare different machine learning models. We have four steps in our methodology: Data collection and preparation, model building, implementation and evaluation. Out of all the models compared, XGBoost Regression yielded the best result for Length of Stay which was our primary output variable and we used the same model for predicting total charges for patients as well. For comparison of models the metric of RMSE and MSE was chosen. Out of all the variables the most impactful predictor for both Length of Stay and Total Charges was CCS Procedure according to our study. Implementation of the system was done through Flask and deployed on the web. The trained model is used for implementation purposes. The system evaluation is done with giving value to test the model whether it gives the desired output or not for predicting length of stay and the total expenses both. |
| Year | 2021 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Vatcharapron Esichiakul |
| Examination Committee(s) | Dailey, Matthew N.;Huynh, Trung Luong |
| Scholarship Donor(s) | AIT Partial Fellowship |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2021 |