Construction the Model on the Breast Cancer Survival Analysis Use Support Vector Machine, Logistic Regression and Decision Tree

Abstract

The aim of the paper is to use data mining technology to establish a classification of breast cancer survival patterns, and offers a treatment decision-making reference for the survival ability of women diagnosed with breast cancer in Taiwan. We studied patients with breast cancer in a specific hospital in Central Taiwan to obtain 1,340 data sets. We employed a support vector machine, logistic regression, and a C5.0 decision tree to construct a classification model of breast cancer patients' survival rates, and used a 10-fold cross-validation approach to identify the model. The results show that the establishment of classification tools for the classification of the models yielded an average accuracy rate of more than 90% for both; the SVM provided the best method for constructing the three categories of the classification system for the survival mode. The results of the experiment show that the three methods used to create the classification system, established a high accuracy rate, predicted a more accurate survival ability of women diagnosed with breast cancer, and could be used as a reference when creating a medical decision-making frame.

A Comparative Study for Modelling the Survival of Breast Cancer Patients in the West of Iran

“…Although the prominent interpretability of these models has made them pioneer for simple medical explanations, but the lower bias and accurate prediction of recently introduced learning algorithms, has stimulated the statistical focus on machine learning methods. The superior performance of these state-of-the-art learning techniques has been confirmed previously in many medical studies (Chao, Yu, Cheng, & Kuo, 2014;Dezfuly & Sajedi, 2015;Habibi, Ahmadi, & Alizadeh, 2015).…”

Platelets, from sample to big data

“…Chilling platelets (4°C) in vitro results in volume increase, spherical deformation, and the formation of lose marginal microtubules and pseudopods [38,39] (Fig. 1.…”

“…Human platelets can be maintained in a discoid shape in the cold, in vitro, using a cell-permeable calcium chelator to attenuate calcium mobilization and cytochalasin B to prevent barbed-end actin assembly [39]. FTIR spectroscopy in northern elephant seals confirms three different thermotropic membrane phase transitions [39]. The microtubules of hibernating mammals are more tolerant to cold, which facilitates the rapid shift from a thrombocytopenic, anticoagulant state during torpor to a normal state [3,4].…”

“…The chilling-induced (0°C, ice water), reversible shape-change in platelets correlates with phosphorylation of myosin, subsequent interaction on actin filaments and free cytosolic calcium increase [40]. Human platelets can be maintained in a discoid shape in the cold, in vitro, using a cell-permeable calcium chelator to attenuate calcium mobilization and cytochalasin B to prevent barbed-end actin assembly [39]. FTIR spectroscopy in northern elephant seals confirms three different thermotropic membrane phase transitions [39].…”

“…Therefore, platelets can act as thermosensors. The ability for priming at peripheral body sites, where temperatures are lower and chances for trauma higher evolved as a protective effect against bleeding, whereas more central body sites (brain and coronary vessels) are more protected against thrombosis [39].Subjecting platelets to chilling changes its surface configuration. In response to cooling, the GPIbα subunit of the vWf receptor complex undergoes clustering and becomes a target for recognition by hepatic macrophage complement receptor type 3 (CR3), which is strongly expressed in liver macrophages, and leads to platelet phagocytosis and clearance.…”