Effective features to classify ovarian cancer data in internet of medical things

2019 ◽  
Vol 159 ◽  
pp. 147-156 ◽  
Author(s):  
Mohamed Elhoseny ◽  
Gui-Bin Bian ◽  
S.K. Lakshmanaprabu ◽  
K. Shankar ◽  
Amit Kumar Singh ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Data ◽  
Ovarian Cancer Data ◽  
Internet Of Medical Things

scholarly journals Effective Features to Classify Ovarian Cancer Data in Internet of Medical Things

2018 ◽  
Author(s):  
Mohamed Elhoseny ◽  
Gui-Bin Bian ◽  
S.K. Lakshmanaprabu ◽  
K. Shankar ◽  
Amit Kumar Singh ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Harmony Search ◽  
Relevant Information ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Self Organizing Maps ◽  
Cancer Data ◽  
Novel Approach ◽  
Ovarian Cancer Data ◽  
Internet Of Medical Things

Ovarian Cancer (OC) is a type of cancer that affects ovaries in women, and is difficult to detect at initial stage due to which it remains as one of the leading causes of cancer death. The ovarian cancer data generated from the Internet of Medical Things (IoMT) was used and a novel approach was proposed for distinguishing the ovarian cancer by utilizing Self Organizing Maps (SOM) and Optimal Recurrent Neural Networks (ORNN). SOM algorithm was utilized for better feature subset selection and was also utilized for separating profitable, understood and intriguing data from huge measures of medical data. In supervised learning techniques, the SOM-based feature selection seems to be a tougher challenge because of the absence of class labels that would guide the search for relevant information to the classifier model. The classification approach can identify ovarian cancer data as benign/malignant. The ovarian cancer detection process can be improved by optimizing the weights of RNN structure using Adaptive Harmony Search Optimization (AHSO). The proposed model in this study can be used to detect cancer at early stages with high accuracy and low Root Mean Square Error (RMSE).

The strong prognostic value of KELIM, a model-based parameter from CA 125 kinetics in ovarian cancer: Data from CALYPSO trial (a GINECO-GCIG study)

2013 ◽  
Vol 130 (2) ◽  
pp. 289-294 ◽  
Author(s):  
Benoit You ◽  
Olivier Colomban ◽  
Mark Heywood ◽  
Chee Lee ◽  
Margaret Davy ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Prognostic Value ◽  
Ca 125 ◽  
Cancer Data ◽  
Model Based ◽  
Ovarian Cancer Data

scholarly journals Application of Modified Generalized–Gamma Mixture Cure Model in the Analysis of Ovarian Cancer Data

2021 ◽  
Vol 2123 (1) ◽  
pp. 012041
Author(s):  
Serifat A. Folorunso ◽  
Timothy A.O. Oluwasola ◽  
Angela U. Chukwu ◽  
Akintunde A. Odukogbe
Keyword(s):  
Ovarian Cancer ◽  
Survival Data ◽  
Information Criterion ◽  
Cure Model ◽  
Modeling And Analysis ◽  
Cancer Data ◽  
Mixture Cure Model ◽  
Generalized Gamma ◽  
Ovarian Cancer Data ◽  
Cure Models

Abstract The modeling and analysis of lifetime for terminal diseases such as cancer is a significant aspect of statistical work. This study considered data from thirty-seven women diagnosed with Ovarian Cancer and hospitalized for care at theDepartment of Obstetrics and Gynecology, University of Ibadan, Nigeria. Focus was on the application of a parametric mixture cure model that can handle skewness associated with survival data – a modified generalized-gamma mixture cure model (MGGMCM). The effectiveness of MGGMCM was compared with existing parametric mixture cure models using Akaike Information Criterion, median time-to-cure and variance of the cure rate. It was observed that the MGGMCM is an improved parametric model for the mixture cure model.

scholarly journals Method of Contraception and Risk of Ovarian Cancer Data

Data in Brief ◽  
2021 ◽  
pp. 107469
Author(s):  
Jacqueline Chesang ◽  
Ann Richardson ◽  
John Potter ◽  
Mary Sneyd ◽  
Pat Coope
Keyword(s):  
Ovarian Cancer ◽  
Cancer Data ◽  
Ovarian Cancer Data

Method of Contraception and Risk of Ovarian Cancer Data

2021 ◽  
Author(s):  
Jacqueline Chesang ◽  
Ann Richardson ◽  
John Potter ◽  
Mary Sneyd ◽  
Pat Coope
Keyword(s):  
Ovarian Cancer ◽  
Cancer Data ◽  
Ovarian Cancer Data

scholarly journals Building a Lung and Ovarian Cancer Data Warehouse

2020 ◽  
Vol 26 (4) ◽  
pp. 303-310
Author(s):  
Canan Eren Atay ◽  
Georgia Garani
Keyword(s):  
Ovarian Cancer ◽  
Data Warehouse ◽  
Cancer Data ◽  
Ovarian Cancer Data
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