Machine learning approach for breast cancer prognosis prediction

2020 ◽  
pp. 41-68
Author(s):  
Bojana R. Andjelkovic Cirkovic
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Breast Cancer Prognosis ◽  
Cancer Prognosis ◽  
Learning Approach ◽  
Prognosis Prediction ◽  
Machine Learning Approach

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 TimeTeller: a New Tool for Precision Circadian Medicine and Cancer Prognosis

2019 ◽  
Author(s):  
Denise Vlachou ◽  
Georg A. Bjarnason ◽  
Sylvie Giacchetti ◽  
Francis Lévi ◽  
David A. Rand
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Heart Disease ◽  
Prognostic Factors ◽  
Circadian Clock ◽  
Biological Sample ◽  
Cancer Prognosis ◽  
Learning Approach ◽  
Therapeutic Outcomes ◽  
Machine Learning Approach

AbstractRecent studies have established that the circadian clock influences onset, progression and therapeutic outcomes in a number of diseases including cancer and heart disease. Therefore, there is a need for tools to measure the functional state of the circadian clock and its downstream targets in patients. We provide such a tool and demonstrate its clinical relevance by an application to breast cancer where we find a strong link between survival and our measure of clock dysfunction. We use a machine-learning approach and construct an algorithm called TimeTeller which uses the multi-dimensional state of the genes in a transcriptomics analysis of a single biological sample to assess the level of circadian clock dysfunction. We demonstrate how this can distinguish healthy from malignant tissues and demonstrate that the molecular clock dysfunction metric is a potentially new prognostic and predictive breast cancer biomarker that is independent of the main established prognostic factors.

scholarly journals Machine Learning techniques in breast cancer prognosis prediction: A primary evaluation

2020 ◽  
Vol 9 (9) ◽  
pp. 3234-3243
Author(s):  
Carlo Boeri ◽  
Corrado Chiappa ◽  
Federica Galli ◽  
Valentina De Berardinis ◽  
Laura Bardelli ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Breast Cancer Prognosis ◽  
Cancer Prognosis ◽  
Machine Learning Techniques ◽  
Prognosis Prediction ◽  
Learning Techniques ◽  
Primary Evaluation

scholarly journals A machine learning approach to predict healthcare cost of breast cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pratyusha Rakshit ◽  
Onintze Zaballa ◽  
Aritz Pérez ◽  
Elisa Gómez-Inhiesto ◽  
Maria T. Acaiturri-Ayesta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Healthcare Cost ◽  
Percentage Error ◽  
Learning Approach ◽  
Early Prediction ◽  
Breast Cancer Patients ◽  
Machine Learning Approach ◽  
Clinical Records

AbstractThis paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients are clustered taking into account the sequences of actions undergoing similar clinical activities and ensuring similar healthcare costs, and (2) a Markov chain is then learned for each group to describe the action-sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase: (1) first, the healthcare cost of a new patient’s treatment is estimated based on the average healthcare cost of its k-nearest neighbors in each group, and (2) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost. Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.

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