Predicting the length of stay in a hospital for a particular disease using machine learning algorithms
This thesis is submitted in partial fulfilment of the requirements for the degree of Master of Engineering in Computer Science and Engineering, 2021.
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Brac University
2021
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| Acceso en liña: | http://hdl.handle.net/10361/15681 |
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10361-156812022-01-26T07:38:46Z Predicting the length of stay in a hospital for a particular disease using machine learning algorithms Imam, Saif Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Length of Stay Linear Regression Boosted Decision Tree Bayesian Regression Machine learning Computer algorithms This thesis is submitted in partial fulfilment of the requirements for the degree of Master of Engineering in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (page 27). Every single day a large number of patients go to the hospital. But the fact is, facilities available in hospitals are not su cient in comparison with the number of patients. The idea of this paper is to o er a prediction system that will be able to say how many days a patient may stay in a hospital. So that the hospital authority may be able to make a better plan to support a large number of patients. In this project, the focus is to give a statistical overview of the recovery time of di erent diseases in Bangladesh and provide a predictive knowledge based on machine learning algorithms about the possible treatment duration for those diseases. We are hopeful that this prediction system will be a great thing for any hospital authority. They will be able to know the estimated staying duration of a patient in hospital and based on that they will prepare plans to provide support to a larger number of patients. In traditional computing, implementing a system like this is quite impossible because the data here does not follow any algorithmic pattern and that's the reason behind introducing machine learning for this particular task. We tried to accumulate all possible treatments and records of patients and run machine learning algorithms like Linear Regression, Boosted Decision Tree, and Bayesian Regression. We compared the accuracy, mean absolute error and root mean squared error for the results we generated from ML Studio using Linear Regression (LR), Bayesian Regression (BR) and Boosted Decision Tree (BDT) and the results are as follows: Accuracy: LR=0.79, BDT=0.72, BR=0.71 Mean absolute error: LR=0.21, BDT=0.24, BR=0.27 Root mean squared error: LR=0.32, BDT=0.36, BR=0.37 Saif Imam M. Computer Science and Engineering 2021-12-01T06:21:45Z 2021-12-01T06:21:45Z 2021 2021-10 Thesis ID 17373001 http://hdl.handle.net/10361/15681 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 27 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Length of Stay Linear Regression Boosted Decision Tree Bayesian Regression Machine learning Computer algorithms |
| spellingShingle |
Length of Stay Linear Regression Boosted Decision Tree Bayesian Regression Machine learning Computer algorithms Imam, Saif Predicting the length of stay in a hospital for a particular disease using machine learning algorithms |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Master of Engineering in Computer Science and Engineering, 2021. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Imam, Saif |
| format |
Thesis |
| author |
Imam, Saif |
| author_sort |
Imam, Saif |
| title |
Predicting the length of stay in a hospital for a particular disease using machine learning algorithms |
| title_short |
Predicting the length of stay in a hospital for a particular disease using machine learning algorithms |
| title_full |
Predicting the length of stay in a hospital for a particular disease using machine learning algorithms |
| title_fullStr |
Predicting the length of stay in a hospital for a particular disease using machine learning algorithms |
| title_full_unstemmed |
Predicting the length of stay in a hospital for a particular disease using machine learning algorithms |
| title_sort |
predicting the length of stay in a hospital for a particular disease using machine learning algorithms |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15681 |
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AT imamsaif predictingthelengthofstayinahospitalforaparticulardiseaseusingmachinelearningalgorithms |
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