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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 54219 (01)
- University: Sharif University of Technology
- Department: Industrial Engineering
- Advisor(s): Akhavan Niaki, Taghi
- Abstract:
- Today, machine learning has many applications in various industries, and healthcare is not an exception. Machine learning algorithms are used for medical diagnosis, make predictions about patients’ future health, newly-discovered treatment effect on patients prediction, drug recommendation system, build risk models and survival estimators and health risk prediction models. One of the topics that has received less attention in the world, especially in Iran, is the prediction of the surgery duration. This is very important because operating rooms in hospitals are the primary source of hospital revenue; We also need to predict the duration of surgery as accurately as possible in order to schedule patients faster and more accurately. Therefore, in this research, the duration of surgery has been predicted using machine learning methods, and an attempt has been made to obtain the highest accuracy with the least number of variables in the dataset. In this regard, two hybrid models have been developed, which increased the prediction accuracy from 92.4% to 96.67%. The slightest increase in accuracy in healthcare is of great importance because it deals with human lives. After predicting each surgery’s duration, the patients are clustered using unsupervised machine learning algorithms, and finally, the obtained clusters are prioritized
- Keywords:
- Clustering ; Data Mining ; Machine Learning ; Prediction ; Surgery Duration
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