Literature DB >> 29243988

Prostate Cancer Probability Prediction By Machine Learning Technique.

Srđan Jović1, Milica Miljković2, Miljan Ivanović2, Milena Šaranović2, Milena Arsić1.   

Abstract

The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

Entities:  

Keywords:  Cancer; Machine Learning; Prediction; Prostate

Mesh:

Year:  2017        PMID: 29243988     DOI: 10.1080/07357907.2017.1406496

Source DB:  PubMed          Journal:  Cancer Invest        ISSN: 0735-7907            Impact factor:   2.176


  10 in total

Review 1.  Artificial intelligence and machine learning in precision and genomic medicine.

Authors:  Sameer Quazi
Journal:  Med Oncol       Date:  2022-06-15       Impact factor: 3.738

2.  Machine learning vs. classic statistics for the prediction of IVF outcomes.

Authors:  Zohar Barnett-Itzhaki; Miriam Elbaz; Rachely Butterman; Devora Amar; Moshe Amitay; Catherine Racowsky; Raoul Orvieto; Russ Hauser; Andrea A Baccarelli; Ronit Machtinger
Journal:  J Assist Reprod Genet       Date:  2020-08-11       Impact factor: 3.412

3.  Predicting Hepatitis B Virus Infection Based on Health Examination Data of Community Population.

Authors:  Ying Wang; Zhicheng Du; Wayne R Lawrence; Yun Huang; Yu Deng; Yuantao Hao
Journal:  Int J Environ Res Public Health       Date:  2019-12-02       Impact factor: 3.390

4.  Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach.

Authors:  Pushpanjali Gupta; Sum-Fu Chiang; Prasan Kumar Sahoo; Suvendu Kumar Mohapatra; Jeng-Fu You; Djeane Debora Onthoni; Hsin-Yuan Hung; Jy-Ming Chiang; Yenlin Huang; Wen-Sy Tsai
Journal:  Cancers (Basel)       Date:  2019-12-12       Impact factor: 6.639

5.  Can machine learning-based analysis of multiparameter MRI and clinical parameters improve the performance of clinically significant prostate cancer diagnosis?

Authors:  Tao Peng; JianMing Xiao; Lin Li; BingJie Pu; XiangKe Niu; XiaoHui Zeng; ZongYong Wang; ChaoBang Gao; Ci Li; Lin Chen; Jin Yang
Journal:  Int J Comput Assist Radiol Surg       Date:  2021-10-22       Impact factor: 2.924

6.  Live-Birth Prediction of Natural-Cycle In Vitro Fertilization Using 57,558 Linked Cycle Records: A Machine Learning Perspective.

Authors:  Yanran Zhang; Lei Shen; Xinghui Yin; Wenfeng Chen
Journal:  Front Endocrinol (Lausanne)       Date:  2022-04-22       Impact factor: 6.055

7.  An enhanced Genetic Folding algorithm for prostate and breast cancer detection.

Authors:  Mohammad A Mezher; Almothana Altamimi; Ruhaifa Altamimi
Journal:  PeerJ Comput Sci       Date:  2022-06-21

8.  Development of a Model Predicting the Outcome of In Vitro Fertilization Cycles by a Robust Decision Tree Method.

Authors:  Kaiyou Fu; Yanrui Li; Houyi Lv; Wei Wu; Jianyuan Song; Jian Xu
Journal:  Front Endocrinol (Lausanne)       Date:  2022-08-24       Impact factor: 6.055

9.  MRI radiomics predicts progression-free survival in prostate cancer.

Authors:  Yushan Jia; Shuai Quan; Jialiang Ren; Hui Wu; Aishi Liu; Yang Gao; Fene Hao; Zhenxing Yang; Tong Zhang; He Hu
Journal:  Front Oncol       Date:  2022-08-30       Impact factor: 5.738

10.  Implementation of Machine Learning Mechanism for Recognising Prostate Cancer through Photoacoustic Signal.

Authors:  G Ramkumar; P Bhuvaneswari; R Radhika; S Saranya; S Vijayalakshmi; M Karpagam; Florin Wilfred
Journal:  Contrast Media Mol Imaging       Date:  2022-09-20       Impact factor: 3.009

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.