Auxiliary Medical Decision System for Prostate Cancer Based on Ensemble Method.

Computational and Mathematical Methods in Medicine
Jia WuYanlin Tan

Abstract

Prostate cancer (PCa) is one of the main diseases that endanger men's health worldwide. In developing countries, due to the large number of patients and the lack of medical resources, there is a big conflict between doctors and patients. To solve this problem, an auxiliary medical decision system for prostate cancer was constructed. The system used six relevant tumor markers as the input features and employed classical machine learning models (support vector machine and artificial neural network). Stacking method aimed at different ensemble models together was used for the reduction of overfitting. 1,933,535 patient information items had been collected from three first-class hospitals in the past five years to train the model. The result showed that the auxiliary medical system could make use of massive data. Its performance is continuously improved as the amount of data increases. Based on the system and collected data, statistics on the incidence of prostate cancer in the past five years were carried out. In the end, influence of diet habit and genetic inheritance for prostate cancer was analyzed. Results revealed the increasing prevalence of PCa and great negative impact caused by high-fat diet and genetic inheritance.

References

Feb 8, 2011·CA: a Cancer Journal for Clinicians·Ahmedin JemalDavid Forman
Sep 16, 2014·International Journal of Cancer. Journal International Du Cancer·Jacques FerlayFreddie Bray
Nov 15, 2017·International Journal of Medical Informatics·Aya AwadJim Briggs
May 6, 2019·Computers in Biology and Medicine·Jia WuYanlin Tan

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Citations

Feb 26, 2021·Computational and Mathematical Methods in Medicine·Jia WuYanlin Tan

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Software Mentioned

SVM
ELR
CAMPAS

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