A Study Of Machine Learning-Based Applications In Patient Identification And Classification Of Prostate Tumors

Jundan WANG; Yanbin LONG

doi:10.47297/wspbdsWSP2752-630509.20240404

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A Study Of Machine Learning-Based Applications In Patient Identification And Classification Of Prostate Tumors

Volume 4, Issue 4

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A Study Of Machine Learning-Based Applications In Patient Identification And Classification Of Prostate Tumors
A Study Of Machine Learning-Based Applications In Patient Identification And Classification Of Prostate Tumors
School of Computer Science, Liaoning University of Science and Technology
Vol. 4, Issue 4, Pages: 54-60(2024)
Published： 25 December 2024 ，
DOI：10.47297/wspbdsWSP2752-630509.20240404
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JUNDAN WANG & YANBIN LONG. (2024). A Study Of Machine Learning-Based Applications In Patient Identification And Classification Of Prostate Tumors. Biomedical Data Science, 4(4), 54-60..
DOI：

JUNDAN WANG & YANBIN LONG. (2024). A Study Of Machine Learning-Based Applications In Patient Identification And Classification Of Prostate Tumors. Biomedical Data Science, 4(4), 54-60.. DOI： 10.47297/wspbdsWSP2752-630509.20240404.

摘要

Abstract

In view of the lack of real-time and efficiency in the screening and classification process of prostate patients in clinical practice

this study is committed to developing an innovative solution. We have constructed four models based on different machine learning algorithms

including BP (back-propagation) neural network

random forest (RF) algorithm

radial basis function (RBF) network and convolutional neural network (CNN)

in order to achieve fast and accurate identification of prostate patient types. These models are designed to distinguish different types of prostate diseases

such as prostatic hyperplasia and prostate adenocarcinoma. Therefore

the convolutional neural network model is expected to provide an efficient and accurate auxiliary tool for early clinical screening of prostate cancer

and has high clinical application value and potential.

关键词

Keywords

prostate hyperplasiaprostate adenocarcinomamachine learningclassification predictionconfusion matrix that

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