Prediction of Length of Stay for Stroke Patients Using Artificial Neural Networks

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Neto, C (Author) Other; Brito, M (Author) Other; Peixoto, H (Author) Other; Lopes, V (Author) FMUP; Abelha, A (Author) Other; Machado, J (Author) Other

Conference proceedings International

Title: WorldCIST (1)

Pages: 212-221

8th World Conference on Information Systems and Technologies, WorldCIST 2020

7 April 2020 through 10 April 2020

Indexing

Scopus - 9 Citations

Other information

Authenticus ID: P-00S-7TS

DOI: 10.1007/978-3-030-45688-7_22

Abstract (EN): Strokes are neurological events that affect a certain area of the brain. Since brain controls fundamental body activities, brain cell deterioration and dead can lead to serious disabilities and poor life quality. This makes strokes the leading cause of disabilities and mortality worldwide. Patients that suffer strokes are hospitalized in order to be submitted to surgery and receive recovery therapies. Thus, it¿s important to predict the length of stay for these patients, since it can be costly to them and their family, as well as to the medical institutions. The aim of this study is to make a prediction on the number of days of patients¿ hospital stays based on information available about the neurological event that happened, the patient¿s health status and surgery details. A neural network was put to test with three attribute subsets with different sizes. The best result was obtained with the subset with fewer features obtaining a RMSE and a MAE of 5.9451 and 4.6354, respectively. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific