Elderly Core Nursing Set (ENCS) - Functional Profile of Older Adults

Citation Author(s):
Javier
Rojo
Lara
Guedes de Pinho
Cesar
Fonseca
Manuel Jose
Lopes
Sumi
Helal
Juan
Hernandez
Jose
Garcia-Alonso
Juan Manuel
Murillo
Submitted by:
Javier Rojo
Last updated:
Thu, 06/09/2022 - 03:54
DOI:
10.21227/6m88-7d42
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Abstract 

Healthcare systems are capable of collecting a significant number of patient health-related parameters. Analyzing them to find the reasons that cause a given disease is challenging. Feature Selection techniques have been used to address this issue---reducing these parameters to a smaller set with the most "determinant" information. However, existing proposals usually focus on classification problems---aimed to detect whether a person is or is not suffering from an illness or from a finite set of illnesses. However, there are many situations in which health professionals need a numerical assessment to quantify the severity of an illness, thus dealing with a regression problem instead. Proposals using Feature Selection here are very limited. This paper examines several Feature Selection techniques to gauge their applicability to the regression-type problems, comparing these techniques by applying them to a real-life scenario on the functional profiles of older adults. Data from 829 functional profiles assessments in 49 residential homes were used in this study. The number of features was reduced from 31 to 25---with a \edit{correlation between inputs and outputs} of 0.99 according to the $R^2$ score and a Mean Square Error (MSE) of 0.11---or to 14 features---with a \edit{correlation} of 0.98 and MSE of 5.73.

Instructions: 

This dataset contains data about 829 assessments of 716 older adults treated in 49 residential homes and medical centers in Portugal. These assessments correspond to the functional profile of older adults using Elderly Core Nursing Set (ENCS).

Funding Agency: 
0499_4IE_PLUS_4_E (Interreg V-A España-Portugal 2014-2020) and RTI2018-094591-B-I00 (MCIU/AEI/FEDER, UE), the FPU19/03965 grant, the Dept. of Economy and Infrastructure of the Gov. of Extremadura (GR18112, IB18030), and the ERDF