scholarly journals The Application of Bayesian Methods in Cancer Prognosis and Prediction

2021 ◽  
Vol 19 (1) ◽  
pp. 1-11
Author(s):  
JIADONG CHU ◽  
NA SUN ◽  
WEI HU ◽  
XUANLI CHEN ◽  
NENGJUN YI ◽  
...  
Keyword(s):  
Bayesian Methods ◽  
Cancer Prognosis ◽  
Prognosis And Prediction

scholarly journals Machine learning applications in cancer prognosis and prediction

2015 ◽  
Vol 13 ◽  
pp. 8-17 ◽  
Author(s):  
Konstantina Kourou ◽  
Themis P. Exarchos ◽  
Konstantinos P. Exarchos ◽  
Michalis V. Karamouzis ◽  
Dimitrios I. Fotiadis
Keyword(s):  
Machine Learning ◽  
Cancer Prognosis ◽  
Prognosis And Prediction ◽  
Machine Learning Applications

scholarly journals Novel Tools for Prostate Cancer Prognosis, Diagnosis, and Follow-Up

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Andreas Dimakakos ◽  
Athanasios Armakolas ◽  
Michael Koutsilieris
Keyword(s):  
Prostate Cancer ◽  
Early Detection ◽  
Therapeutic Response ◽  
Specific Antigen ◽  
Cancer Prognosis ◽  
Paper Briefly ◽  
Future Directions ◽  
Prognosis And Prediction ◽  
Prostate Cancer Prognosis

Prostate-specific antigen (PSA) is the main diagnostic tool when it comes to prostate cancer but it possesses serious limitations. Therefore, there is an urgent need for more sensitive and specific biomarkers for prostate cancer prognosis and patient follow-up. Recent advances led to the discovery of many novel diagnostic/prognostic techniques and provided us with many worthwhile candidates. This paper briefly reviews the most promising biomarkers with respect to their implementation in screening, early detection, diagnostic confirmation, prognosis, and prediction of therapeutic response or monitoring disease and recurrence; and their use as possible therapeutic targets. This review also examines the possible future directions in the field of prostate cancer marker research.

Machine Learning Is the Future for Lung Cancer Prognosis and Prediction

2020 ◽  
pp. 176-196
Author(s):  
Ayshwarya Balakumar ◽  
Senthil S.
Keyword(s):  
Lung Cancer ◽  
Back Propagation ◽  
Hiv Infections ◽  
Cancer Prognosis ◽  
Ant Lion Optimization ◽  
Prognosis And Prediction ◽  
Characteristic Features ◽  
The One ◽  
Propagation Network ◽  
Lion Optimization Algorithm

Lung cancer is one of the major reasons for the death if it is not diagnosed in the early stages of cancer. It is the one among the most dreadful disease which affects in the lungs function. It can be identified only after the disease spread into the deeper parts of the lungs and then only it will make a life threading problem. Lung cancer prognosis which was done based on the various parameters such as age, sex, condition of smoking, duration of smoking and count of smoking per day. The proceedings were done using the algorithm for the time to first cigarette after awakening which is represented as TTFC. The expert doctor says that the back-propagation network is a great deal in the recognition of the lung cancer without any involvement by them. This research is based on the classification of lung cancer and its stages using the establishment of the BPN and predicts the recurrence. Similarly, with this BPN, an algorithm that is inspired from its habitat known as ant lion optimization algorithm is also used in the optimization of weights and parameters of the BPN. The use of the ALO algorithm provides an improved convergence mechanism by improving the proposed technique's accuracy. The use of this proposed method with the BPN optimizes the network and the ALO optimizer provides an accurate prediction of the lung cancer by the earlier stage and even predicts the changes for reoccurrence after diagnosis. The prognosis analysis was made by the various comparative study between the characteristic features of HIV and the unaffected person using the algorithm such as the Wilcoxon rank-sum test. This algorithm will continuously classify the viral load and CD4 count which is based on factors such as age, sex, and smoking activities. It will be useful for early diagnosis and future prediction. Lung cancer rates can be analyzed based on the incident rates of affected and unaffected persons to HIV infections.

Sign in / Sign up

Export Citation Format

Share Document