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

• Published in: IEEE journal of biomedical and health informatics Vol. 26; no. 3; pp. 1309 - 1317
• Main Authors: Petinrin, Olutomilayo Olayemi, Li, Xiangtao, Wong, Ka-Chun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Area Under Curve; Blood circulation; Castration; Classifiers; Docetaxel; Gaussian process; Gaussian process classifier; Gaussian processes; Humans; Kernel; machine learning; Male; Metastases; Metastasis; metastatic castration-resistant prostate cancer; multi-cohort data; Normal Distribution; Particle swarm optimization; Patients; Prevention; Prostate cancer; Prostatic Neoplasms, Castration-Resistant - drug therapy; ROC Curve; Statistical analysis; Swarm intelligence; Training; Tumors; Urban areas
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2021.3103989

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.