Modeling and Inference with Feature Importance for Assessing the Quality of Sleep Among Chronic Kidney Disease Patients

• Activity Number: 340 - SPEED: SPAAC SESSION III
• Type: Topic-Contributed
• Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Surani Lakshima Matharaarachchi* and Saman Muthukumarana and Mike Domaratzki and Chamil Marasinghe and Varuni Tennakoon
• Companies: University of Manitoba and University of Manitoba and University of Manitoba and University of Sri Jayewardenepura and University of Sri Jayewardenepura
• Keywords: Chronic Kidney Disease; Sleep Quality; Classification; Decision Trees; Feature Importance; SMOTE

Abstract

Chronic Kidney Disease (CKD) is a progressive and irreversible loss of kidney function. Data mining concepts may be used in the healthcare industry to obtain hidden clinical information for a reliable and effective decision-making process. These advanced learning methods would identify the relationships and patterns that will help classify factors that affect the poor sleep quality of CKD patients. Poor sleep quality is a critical issue for CKD individuals, negatively affecting immunity, cognitive functions, and emotional demonstrations. This study aims to find the clinical features affecting the sleep quality of CKD patients. Decision tree-based methods are used to identify the impact of each feature to predict sleep quality. The predictive results are compared with different classification models as well. Furthermore, two re-sampling techniques, Synthetic Minority Oversampling and Random Oversampling are also used to reduce the impact of the imbalanced nature of the dataset. We further discuss how these results agree with the clinically relevant features determined by the physicians.