Building an Effective Classification Model for Breast Cancer Patient Response Data

2015 ◽  
Author(s):  
Brian Heredia ◽  
Taghi M. Khoshgoftaar ◽  
Alireza Fazelpour ◽  
David J. Dittman
Keyword(s):  
Breast Cancer ◽  
Cancer Patient ◽  
Breast Cancer Patient ◽  
Classification Model ◽  
Patient Response ◽  
Response Data

scholarly journals MT4-MMP and EGFR expression levels are key biomarkers for breast cancer patient response to chemotherapy and erlotinib

2017 ◽  
Vol 116 (6) ◽  
pp. 742-751 ◽  
Author(s):  
Cassandre Yip ◽  
Pierre Foidart ◽  
Joan Somja ◽  
Alice Truong ◽  
Mehdi Lienard ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patient ◽  
Breast Cancer Patient ◽  
Patient Response ◽  
Expression Levels ◽  
Egfr Expression ◽  
Response To Chemotherapy

scholarly journals Data Mining Approach for Breast Cancer Patient Recovery

2017 ◽  
Vol 5 (1) ◽  
pp. 36-71 ◽  
Author(s):  
Tresna Maulana Fahrudin ◽  
Iwan Syarif ◽  
Ali Ridho Barakbah
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Cancer Patient ◽  
Breast Cancer Patient ◽  
Information Gain ◽  
Patient Data ◽  
Classification Model ◽  
Cancer Type ◽  
Cancer Data ◽  
Increased Risk

Breast cancer is the second highest cancer type which attacked Indonesian women. There are several factors known related to encourage an increased risk of breast cancer, but especially in Indonesia that factors often depends on the treatment routinely. This research examines the determinant factors of breast cancer and measures the breast cancer patient data to build the useful classification model using data mining approach.The dataset was originally taken from one of Oncology Hospital in East Java, Indonesia, which consists of 1097 samples, 21 attributes and 2 classes. We used three different feature selection algorithms which are Information Gain, Fisher’s Discriminant Ratio and Chi-square to select the best attributes that have great contribution to the data. We applied Hierarchical K-means Clustering to remove attributes which have lowest contribution. Our experiment showed that only 14 of 21 original attributes have the highest contribution factor of the breast cancer data. The clustering algorithmdecreased the error ratio from 44.48% (using 21 original attributes) to 18.32% (using 14 most important attributes).We also applied the classification algorithm to build the classification model and measure the precision of breast cancer patient data. The comparison of classification algorithms between Naïve Bayes and Decision Tree were both given precision reach 92.76% and 92.99% respectively by leave-one-out cross validation. The information based on our data research, the breast cancer patient in Indonesia especially in East Java must be improved by the treatment routinely in the hospital to get early recover of breast cancer which it is related with adherence of patient.

THE RELATION OF COPPING STRATEGY BY LOOKING SOCIAL SUPPORT WITH STRESS LEVEL OF BREAST CANCER PATIENT WHO GONE TROUGH CHEMOTHERAPY

2018 ◽  
Vol 1 (1) ◽  
pp. 51-54
Author(s):  
Sri Burhani Putri
Keyword(s):  
Breast Cancer ◽  
Social Support ◽  
Cancer Patient ◽  
Breast Cancer Patient ◽  
Cross Sectional Study ◽  
P Value ◽  
Spiritual Support ◽  
Cross Sectional ◽  
Breast Cancer Chemotherapy ◽  
Patient Stress

Breast cancer is one of the most common illness that killed woman. One of the therapy to cure breast cancer is chemotherapy. Chemotherapy has side effect either physical and psychology, that caused people who’s in chemo therapy, prone to stress. Stress effected by many factors, such as characteristic and chopping strategy that patient has been using. The aim of this research is to get a perspective about the relation of characteristic and chopping strategy with breast cancer patient stress, whose in chemo therapy. This research using cross sectional study and taking sample by using accidental sampling method. The data analyzed by using bavariat and multivariat with variable result shows that breast cancer patient stress who has chemo therapy realted to age characteristic (p value = 0.00) the time since they diagnosed with cancer (pvalue = 0.03), how long they have chemo therapy (pvalue = 0.00) and chopping strategyby looking social support (pvalue = 0.00) looking for spiritual (pvalue = 0.00) with dominan variable which related to stress is chopping strategy to looking spiritual support (coeffecients B = -1.139).   Key words : Breast cancer, chemotherapy, stress  

scholarly journals Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mindaugas Morkunas ◽  
Dovile Zilenaite ◽  
Aida Laurinaviciene ◽  
Povilas Treigys ◽  
Arvydas Laurinavicius
Keyword(s):  
Breast Cancer ◽  
Breast Carcinoma ◽  
Cancer Patient ◽  
Breast Cancer Patient ◽  
Cox Regression ◽  
Regional Lymph Node ◽  
Histological Grade ◽  
Biological Information ◽  
Lymph Node Status ◽  
Bright Field

AbstractWithin the tumor microenvironment, specifically aligned collagen has been shown to stimulate tumor progression by directing the migration of metastatic cells along its structural framework. Tumor-associated collagen signatures (TACS) have been linked to breast cancer patient outcome. Robust and affordable methods for assessing biological information contained in collagen architecture need to be developed. We have developed a novel artificial neural network (ANN) based approach for tumor collagen segmentation from bright-field histology images and have tested it on a set of tissue microarray sections from early hormone receptor-positive invasive ductal breast carcinoma stained with Sirius Red (1 core per patient, n = 92). We designed and trained ANNs on sets of differently annotated image patches to segment collagen fibers and extracted 37 features of collagen fiber morphometry, density, orientation, texture, and fractal characteristics in the entire cohort. Independent instances of ANN models trained on highly differing annotations produced reasonably concordant collagen segmentation masks and allowed reliable prognostic Cox regression models (with likelihood ratios 14.11–22.99, at p-value < 0.05) superior to conventional clinical parameters (size of the primary tumor (T), regional lymph node status (N), histological grade (G), and patient age). Additionally, we noted statistically significant differences of collagen features between tumor grade groups, and the factor analysis revealed features resembling the TACS concept. Our proposed method offers collagen framework segmentation from bright-field histology images and provides novel image-based features for better breast cancer patient prognostication.

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