Machine Learning in Prediction of Bladder Cancer on Clinical Laboratory Data

I. Jung Tsai, Wen Chi Shen, Chia Ling Lee, Horng Dar Wang, Ching Yu Lin

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Bladder cancer has been increasing globally. Urinary cytology is considered a major screening method for bladder cancer, but it has poor sensitivity. This study aimed to utilize clinical laboratory data and machine learning methods to build predictive models of bladder cancer. A total of 1336 patients with cystitis, bladder cancer, kidney cancer, uterus cancer, and prostate cancer were enrolled in this study. Two-step feature selection combined with WEKA and forward selection was performed. Furthermore, five machine learning models, including decision tree, random forest, support vector machine, extreme gradient boosting (XGBoost), and light gradient boosting machine (GBM) were applied. Features, including calcium, alkaline phosphatase (ALP), albumin, urine ketone, urine occult blood, creatinine, alanine aminotransferase (ALT), and diabetes were selected. The lightGBM model obtained an accuracy of 84.8% to 86.9%, a sensitivity 84% to 87.8%, a specificity of 82.9% to 86.7%, and an area under the curve (AUC) of 0.88 to 0.92 in discriminating bladder cancer from cystitis and other cancers. Our study provides a demonstration of utilizing clinical laboratory data to predict bladder cancer.

Original languageEnglish
Article number203
JournalDiagnostics
Volume12
Issue number1
DOIs
Publication statusPublished - Jan 2022

Keywords

  • Bladder cancer
  • Clinical laboratory data
  • Feature selection
  • Machine learning

ASJC Scopus subject areas

  • Clinical Biochemistry

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