TY - GEN
T1 - Comparing machine learning clustering with latent class analysis on cancer symptoms' data
AU - Papachristou, Nikolaos
AU - Miaskowski, Christine
AU - Barnaghi, Payam
AU - Maguire, Roma
AU - Farajidavar, Nazli
AU - Cooper, Bruce
AU - Hu, Xiao
N1 - © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PY - 2016/11/9
Y1 - 2016/11/9
N2 - Symptom Cluster Research is a major topic in Cancer Symptom Science. In spite of the several statistical and clinical approaches in this domain, there is not a consensus on which method performs better. Identifying a generally accepted analytical method is important in order to be able to utilize and process all the available data. In this paper we report a secondary analysis on cancer symptom data, comparing the performance of five Machine Learning (ML) clustering algorithms in doing so. Based on how well they separate specific subsets of symptom measurements we select the best of them and proceed to compare its performance with the Latent Class Analysis (LCA) method. This analysis is a part of an ongoing study for identifying suitable Machine Learning algorithms to analyse and predict cancer symptoms in cancer treatment.
AB - Symptom Cluster Research is a major topic in Cancer Symptom Science. In spite of the several statistical and clinical approaches in this domain, there is not a consensus on which method performs better. Identifying a generally accepted analytical method is important in order to be able to utilize and process all the available data. In this paper we report a secondary analysis on cancer symptom data, comparing the performance of five Machine Learning (ML) clustering algorithms in doing so. Based on how well they separate specific subsets of symptom measurements we select the best of them and proceed to compare its performance with the Latent Class Analysis (LCA) method. This analysis is a part of an ongoing study for identifying suitable Machine Learning algorithms to analyse and predict cancer symptoms in cancer treatment.
KW - Symptom Cluster Research
KW - machine learning
KW - cancer symptom science
KW - learning algorithms
KW - cancer treatment
U2 - 10.1109/HIC.2016.7797722
DO - 10.1109/HIC.2016.7797722
M3 - Conference contribution book
SN - 9781509011674
SP - 162
EP - 166
BT - Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT), 2016 IEEE
PB - IEEE
CY - Piscataway, NJ.
ER -
