scholarly journals Machine learning prediction of breast cancer survival using age, sex, length of stay, mode of diagnosis and location of cancer

2021 ◽  
Author(s):  
Hilary I. Okagbue ◽  
Patience I. Adamu ◽  
Pelumi E. Oguntunde ◽  
Emmanuela C. M. Obasi ◽  
Oluwole A. Odetunmibi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Length Of Stay ◽  
Cancer Survival ◽  
Breast Cancer Survival

scholarly journals Analysis of Prognostic Factors Influencing Survival and Recurrence in Breast Cancer: A Hybrid Machine Learning Approach

2021 ◽  
Author(s):  
Jae Bin Lee ◽  
Jihye Choi ◽  
Mi Sun An ◽  
Jong-Yeup Kim ◽  
Seong Uk Kwon ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Prognostic Factors ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Cancer Prognosis ◽  
Learning Approach ◽  
Factors Influencing ◽  
Machine Learning Approach ◽  
History Of

Abstract Purpose: The present study sought to identify prognostic factors for breast cancer survival and recurrence using a machine learning approach and electronic medical record data.Methods: We used a machine learning technique called feature selection to identify factors influencing breast cancer prognosis, and factors affecting survival and recurrence in a Cox regression model. Results: History of relapse, type of surgery, diagnostic route, SEER stage, and hormone therapy all affected breast cancer survival. Recurrence of breast cancer was affected by age, history of diabetes, breast reconstruction, pain, breast lumps, nipple discharge, and the presence of other symptoms. According to the survival analysis based on feature selection, patients with diabetes had a significantly higher risk of early recurrence of breast cancer (hazard ratio, 4.8; 95% confidence interval, 2.04–11.2, p < 0.05). Conclusions: The present study identified several factors associated with breast cancer prognosis. While survival was affected by the diagnostic route, recurrence was primarily influenced by breast cancer symptoms and other underlying health conditions. A more accurate and standardized model considering time-to-event data could be developed in the future to evaluate prognostic factors and predict prognoses, and for clinical validation

scholarly journals An Artificial Intelligence-Enabled Pipeline for Medical Domain: Malaysian Breast Cancer Survivorship Cohort as a Case Study

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1492
Author(s):  
Mogana Darshini Ganggayah ◽  
Sarinder Kaur Dhillon ◽  
Tania Islam ◽  
Foad Kalhor ◽  
Teh Chean Chiang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Survival Prediction ◽  
Automated Scoring

Automated artificial intelligence (AI) systems enable the integration of different types of data from various sources for clinical decision-making. The aim of this study is to propose a pipeline to develop a fully automated clinician-friendly AI-enabled database platform for breast cancer survival prediction. A case study of breast cancer survival cohort from the University Malaya Medical Centre was used to develop and evaluate the pipeline. A relational database and a fully automated system were developed by integrating the database with analytical modules (machine learning, automated scoring for quality of life, and interactive visualization). The developed pipeline, iSurvive has helped in enhancing data management as well as to visualize important prognostic variables and survival rates. The embedded automated scoring module demonstrated quality of life of patients whereas the interactive visualizations could be used by clinicians to facilitate communication with patients. The pipeline proposed in this study is a one-stop center to manage data, to automate analytics using machine learning, to automate scoring and to produce explainable interactive visuals to enhance clinician-patient communication along the survivorship period to modify behaviours that relate to prognosis. The pipeline proposed can be modelled on any disease not limited to breast cancer.

scholarly journals Prediction of Breast Cancer Survival by Machine Learning Methods: An Application of Multiple Imputation

2021 ◽  
Author(s):  
Hadi LOTFNEZHAD AFSHAR ◽  
Nasrollah JABBARI ◽  
Hamid Reza KHALKHALI ◽  
Omid ESNAASHARI
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Missing Data ◽  
Multiple Imputation ◽  
Cancer Survival ◽  
Developed Countries ◽  
Breast Cancer Survival ◽  
Machine Learning Techniques ◽  
Breast Cancer Dataset ◽  
Original Dataset

Background: The low breast cancer survival rates in less developed countries are critical. The machine learning techniques predict cancers survival with high accuracy. Missing data are the most important limitation for using the highest potential of these techniques to predict cancers survival. Multiple imputation (MI) was implemented and analyzed in detail to impute the missing data of a breast cancer dataset. Methods: The dataset was from The Omid Treatment and Research Center Urmia, Iran between Jan 2006 and Dec 2012 and had information from 856 women. The algorithms such as C5 and repeated incremental pruning to produce error reduction were applied on the imputed versions of the original dataset and the non-imputed dataset to predict and extract clinical rules, respectively. Results: The findings showed the performance of C5 in all the evaluation criteria including accuracy (84.42%), sensitivity (92.21%), specificity (64%), Kappa statistic (59.06%), and the area under the receiver operator characteristic (ROC) curve (0.84), was improved after imputation. Conclusion: The dataset of the present study met the requirements for using the multiple imputation method. The extracted rules after the application of MI were more comprehensive and contained knowledge that is more clinical. However, the clinical value of the extracted rules after filling in the missing data did not noticeably increase.

scholarly journals Machine learning models in breast cancer survival prediction

2016 ◽  
Vol 24 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Mitra Montazeri ◽  
Mohadeseh Montazeri ◽  
Mahdieh Montazeri ◽  
Amin Beigzadeh
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Survival Prediction ◽  
Learning Models ◽  
Machine Learning Models

The influence of polymorphisms in genes related to oxidative stress on breast cancer survival in a German case-control study

2010 ◽  
Vol 72 (08/09) ◽  
Author(s):  
P Seibold ◽  
R Hein ◽  
O Popanda ◽  
D Flesch-Janys ◽  
P Schmezer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Oxidative Stress ◽  
Cancer Survival ◽  
Case Control Study ◽  
Case Control ◽  
Breast Cancer Survival ◽  
Control Study
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