Citation

Law, Theng Jia and Ting, Choo Yee and Zakariah, Helmi (2024) Detecting Need-Attention Patients using Machine Learning. JOIV : International Journal on Informatics Visualization, 8 (2). p. 803. ISSN 2549-9610

Text Microsoft Word - Theng 2277-CI-AAP.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

In healthcare, detecting patients who need immediate attention is difficult. Identifying the critical variables is challenging in patient detection because human intervention in variable selection is required. Consequently, patients who need immediate attention often experience prolonged waiting times. Researchers have investigated various approaches to identify those who require attention. One of the techniques is leveraging Artificial Intelligence (AI). However, identifying the optimal feature set and predictive model is complex. Therefore, this study has attempted to (i) identify the critical features and (ii) develop and evaluate predictive models in detecting those who need attention. The dataset is collected from one of the healthcare companies. The dataset collected contains 67 variables and 51102 records. It consists of patient information and questionnaires answered by each participant registered in the Selangor Saring Program. Important features were identified in detecting those who need attention on treated data. Multiple classifiers were developed due to their simplicity. The models were evaluated before and after hyperparameter tuning based on accuracy, precision, recall, F1-score, Geometric Mean, and Area Under the Curve. The findings showed that the Stacking Classifier produced the highest accuracy (69.9%) when using the blood dataset. In contrast, Extreme Gradient Boosting achieved the highest accuracy (81.7%) when the urine dataset was used. This work can be extended to explore the incorporation of Points of Interest and geographical data near patients’ residences and study other ensemble models to enhance the performance of detecting those who need attention

Item Type:	Article
Uncontrolled Keywords:	Artificial intelligence, machine learning.
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 Jul 2024 07:24
Last Modified:	02 Jul 2024 07:24
URII:	http://shdl.mmu.edu.my/id/eprint/12552

Downloads

Edit (login required)