Particle Swarm Optimized Gaussian Process Classifier for Treatment Discontinuation Prediction in Multicohort Metastatic Castration-Resistant Prostate Cancer Patients

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Scopus Citations
View graph of relations

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)1309-1317
Journal / PublicationIEEE Journal of Biomedical and Health Informatics
Volume26
Issue number3
Online published11 Aug 2021
Publication statusPublished - Mar 2022

Abstract

Prostate cancer is the second leading cancer in men, according to the WHO world cancer report. Its prevention and treatment demand proper attention. Despite numerous attempts for disease prevention, prostate tumours can still become metastatic by blood circulation to other organs. Several treatments have been adopted. However, findings show that the docetaxel treatment induces adverse reactions in patients. Particle Swarm Optimized Gaussian Process Classifier (PSO-GPC) is proposed to determine when to discontinue treatment. Based on three cohorts of prostate cancer patients, we propose and compare several classifiers for the best performance in determining treatment discontinuation. Given the data skewness and class imbalance, the models are evaluated based on both the area under receiver operating characteristics curve (AUC) and area under precision recall curve (AUPRC). With the AUCs ranging between 0.6717 - 0.8499, and AUPRCs ranging between 0.1392 - 0.5423, PSO-GPC performs better than the state-of-the-art. We have carried out statistical analysis for ranking methods and analyzed independent cohort data with PSO-GPC, demonstrating its unbiased performance. A proper determination of treatment discontinuation in metastatic castration-resistant prostate cancer patients will reduce the mortality rate in cancer patients.

Research Area(s)

  • Gaussian process classifier, machine learning, metastatic castration-resistant prostate cancer, multi-cohort data, particle swarm optimization

Citation Format(s)

Particle Swarm Optimized Gaussian Process Classifier for Treatment Discontinuation Prediction in Multicohort Metastatic Castration-Resistant Prostate Cancer Patients. / Petinrin, Olutomilayo Olayemi; Li, Xiangtao; Wong, Ka-Chun.
In: IEEE Journal of Biomedical and Health Informatics, Vol. 26, No. 3, 03.2022, p. 1309-1317.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review