scholarly journals Pemodelan Faktor-Faktor yang Mempengaruhi Jenis Kanker Payudara Menggunakan Regresi Logistik Biner (Kasus : Pasien Penderita Kanker Payudara di RSUP Dr. Wahidin Sudirohusodo tahun 2016).

2019 ◽  
Vol 1 (3) ◽  
pp. 40
Author(s):  
Titi Kurnianti HR ◽  
Muhammad Nadjib Bustan ◽  
R. Ruliana
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Statistical Analysis ◽  
Binary Logistic Regression ◽  
Breast Cancer Patients ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Independent Variables ◽  
Continuous Scale ◽  
Categorical Scale

Abstrak Regresi logistik adalah suatu metode analisis statistik yang diterapkan untuk memodelkan variabel dependen yang memiliki dua kategori atau lebih dengan satu atau lebih variabel independen. Regresi Logistik biner merupakan suatu analisis statistika yang digunakan untuk menganalisis hubungan antara satu atau lebih peubah bebas dengan peubah respon yang bersifat biner atau  dichotomous. Peubah bebas pada regresi logistik dapat berupa peubah skala kategorik maupun peubah yang skala kontinu sedangkan peubah respon berupa peubah berskala kategorik. Regresi Logistik Biner dapat diterapkan pada kasus kesehatan, khususnya pada penelitian ini yaitu mengenai kanker payudara. Sesuai uraian diatas maka penulis bermaksud untuk mengkaji dan melakukan penelitian  tentang Pemodelan Faktor-Faktor yang Mempengaruhi Jenis Kanker Payudara Menggunakan Regresi Logistik Biner (Kasus : Pasien Penderita Kanker Payudara di Rumah Sakit Umum Pusat Dr. Wahidin Sudirohusodo). Dari hasil analisis didapatkan bahwa peubah penjelas yang berpengaruh nyata terhadap jenis keganasan kanker terhadap pasien penderita kanker payudara adalah peubah Kemoterapi (X2) dan peubah Metastase (X5) yang masing-masing memiliki nilai odds rasio sebesar 0,17 dan 6,16.  Kata kunci : Kanker Payudara, Regresi Logistik, Regresi Logistik Biner. Abstract Logistic regression is a method of statistical analysis that is applied to model the dependent variable which has two or more categories with one or more independent variables. Binary Logistic Regression is a statistical analysis that is used to analyze the relationship between one or more independent variables with variable binary or dichotomous responses. The free variables in logistic regression can be either categorical scale or continuous scale variables while the response variables are categorical scale variables. Binary Logistic Regression can be applied to health cases, especially in this study, namely breast cancer. In accordance with the description above, the author intends to study and conduct research on Modeling Factors Affecting Types of Breast Cancer Using Binary Logistic Regression (Case: Patients with Breast Cancer Patients at Dr. Wahidin Sudirohusodo Central General Hospital). From the results of the analysis it was found that the explanatory variables that significantly affected the type of cancer malignancy in patients with breast cancer were Chemotherapy variables (X2) and Metastase variables (X5), each of which had odds ratio values of 0.17 and 6.16. Keywords: Breast Cancer, Logistic Regression, Binary Logistic Regression.

scholarly journals MODEL DAN PELUANG TERJADINYA HUJAN BERDASARKAN KELEMBABAN MENGGUNAKAN REGRESI LOGISTIK BINER DI KABUPATEN MANOKWARI

2020 ◽  
Vol 16 (1) ◽  
pp. 34-37
Author(s):  
Inayatul Farach Ardianty ◽  
Indah Ratih Anggriyani
Keyword(s):  
Logistic Regression ◽  
Binary Logistic Regression ◽  
Regression Estimation ◽  
Response Variable ◽  
Nominal Scale ◽  
Explanatory Variables ◽  
Logit Transformation ◽  
Scale Response ◽  
The Relationship ◽  
Categorical Scale

Manokwari Regrency has a tropical climate. This results in significant rainfall. One factor that stimulates rain is humidity. By using binary logistic regression, the model an chance of rainfall based on humidity can be determined. Logistic regression analysis is used to determine the relationship between categorical  scale response variables and numeric or categoric scale explanatory variables. If response variable used is nominal scale with  two possoble value (0 and 1), then it is called binary logistic regression. Estimation of the model  is done by logit  transformation. The model produce in this  study is g(x) = -23.443 + 0.289  humidity. The accuracy of the model is 70.4 percent and the probability of rain for humidity lowering one unit will be reduced to 0.74.

scholarly journals Serum Long Non-Coding RNAs PVT1, HOTAIR, and NEAT1 as Potential Biomarkers in Egyptian Women with Breast Cancer

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 301
Author(s):  
Amal Ahmed Abd El-Fattah ◽  
Nermin Abdel Hamid Sadik ◽  
Olfat Gamil Shaker ◽  
Amal Mohamed Kamal ◽  
Nancy Nabil Shahin
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Cancer Patients ◽  
Breast Cancer Diagnosis ◽  
Serum Levels ◽  
Breast Cancer Patients ◽  
Multivariate Logistic Regression ◽  
Expression Levels ◽  
Non Coding Rnas ◽  
Pai 1

Long non-coding RNAs play an important role in tumor growth, angiogenesis, and metastasis in several types of cancer. However, the clinical significance of using lncRNAs as biomarkers for breast cancer diagnosis and prognosis is still poorly investigated. In this study, we analyzed the serum expression levels of lncRNAs PVT1, HOTAIR, NEAT1, and MALAT1, and their associated proteins, PAI-1, and OPN, in breast cancer patients compared to fibroadenoma patients and healthy subjects. Using quantitative real-time PCR (qRT-PCR), we compared the serum expression levels of the four circulating lncRNAs in patients with breast cancer (n = 50), fibroadenoma (n = 25), and healthy controls (n = 25). The serum levels of PAI-1 and OPN were measured using ELISA. Receiveroperating-characteristic (ROC) analysis and multivariate logistic regression were used to evaluate the diagnostic value of the selected parameters. The serum levels of HOTAIR, PAI-1, and OPN were significantly higher in breast cancer patients compared to controls and fibroadenoma patients. The serum level of PVT1 was significantly higher in breast cancer patients than in the controls, while that of NEAT1 was significantly lower in breast cancer patients compared to controls and fibroadenoma patients. Both ROC and multivariate logistic regression analyses revealed that PAI-1 has the greatest power in discriminating breast cancer from the control, whereas HOTAIR, PAI-1, and OPN have the greatest power in discriminating breast cancer from fibroadenoma patients. In conclusion, our data suggest that the serum levels of PVT1, HOTAIR, NEAT1, PAI-1, and OPN could serve as promising diagnostic biomarkers for breast cancer.

scholarly journals Factors affecting breast cancer patients' need for genetic risk information: From information insufficiency to information need

2019 ◽  
Vol 28 (3) ◽  
pp. 543-557 ◽  
Author(s):  
Soo Jung Hong ◽  
Barbara Biesecker ◽  
Jennifer Ivanovich ◽  
Melody Goodman ◽  
Kimberly A. Kaphingst
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Genetic Risk ◽  
Risk Information ◽  
Breast Cancer Patients ◽  
Information Need ◽  
Factors Affecting ◽  
Genetic Risk Information

Identifying the Factors Affecting the Probability of Accident Severity in Community Road - Focusing on Seoul City

2014 ◽  
Vol 641-642 ◽  
pp. 860-865
Author(s):  
You Jin Lim ◽  
Hak Ryong Moon ◽  
Won Pyoung Kang
Keyword(s):  
Logistic Regression ◽  
Binary Logistic Regression ◽  
Regression Technique ◽  
Road Accidents ◽  
Factors Affecting ◽  
Accident Severity

Since a variety of factors are associated with crash occurrence, the analysis of causes of crash is a hard task for traffic researchers and engineers. This study was attempted to identify factors affecting severity of the community road accidents. In particular, our analyses were focused on the community road accidents. A binary logistic regression technique was adopted for the analyses. The results showed that pedestrians of 65 years or older, cloudy, fence (sidewalk/driveway barrier), drivers of 24 years or younger, left/right turning, female pedestrian, non-business vehicle were dominant factors for the severity.

scholarly journals Predicting the Level of Tumor-Infiltrating Lymphocytes in Patients With Breast Cancer: Usefulness of Mammographic Radiomics Features

2021 ◽  
Vol 11 ◽  
Author(s):  
Hongwei Yu ◽  
Xianqi Meng ◽  
Huang Chen ◽  
Jian Liu ◽  
Wenwen Gao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Feature Selection ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Training Dataset ◽  
Validation Dataset ◽  
Gray Level ◽  
Breast Cancer Patients ◽  
Short Run

ObjectivesThis study aimed to investigate whether radiomics classifiers from mammography can help predict tumor-infiltrating lymphocyte (TIL) levels in breast cancer.MethodsData from 121 consecutive patients with pathologically-proven breast cancer who underwent preoperative mammography from February 2018 to May 2019 were retrospectively analyzed. Patients were randomly divided into a training dataset (n = 85) and a validation dataset (n = 36). A total of 612 quantitative radiomics features were extracted from mammograms using the Pyradiomics software. Radiomics feature selection and radiomics classifier were generated through recursive feature elimination and logistic regression analysis model. The relationship between radiomics features and TIL levels in breast cancer patients was explored. The predictive capacity of the radiomics classifiers for the TIL levels was investigated through receiver operating characteristic curves in the training and validation groups. A radiomics score (Rad score) was generated using a logistic regression analysis method to compute the training and validation datasets, and combining the Mann–Whitney U test to evaluate the level of TILs in the low and high groups.ResultsAmong the 121 patients, 32 (26.44%) exhibited high TIL levels, and 89 (73.56%) showed low TIL levels. The ER negativity (p = 0.01) and the Ki-67 negative threshold level (p = 0.03) in the low TIL group was higher than that in the high TIL group. Through the radiomics feature selection, six top-class features [Wavelet GLDM low gray-level emphasis (mediolateral oblique, MLO), GLRLM short-run low gray-level emphasis (craniocaudal, CC), LBP2D GLRLM short-run high gray-level emphasis (CC), LBP2D GLDM dependence entropy (MLO), wavelet interquartile range (MLO), and LBP2D median (MLO)] were selected to constitute the radiomics classifiers. The radiomics classifier had an excellent predictive performance for TIL levels both in the training and validation sets [area under the curve (AUC): 0.83, 95% confidence interval (CI), 0.738–0.917, with positive predictive value (PPV) of 0.913; AUC: 0.79, 95% CI, 0.615–0.964, with PPV of 0.889, respectively]. Moreover, the Rad score in the training dataset was higher than that in the validation dataset (p = 0.007 and p = 0.001, respectively).ConclusionRadiomics from digital mammograms not only predicts the TIL levels in breast cancer patients, but can also serve as non-invasive biomarkers in precision medicine, allowing for the development of treatment plans.

scholarly journals Comparison of Imputation Methods on Retrospective Breast Cancer Data in Tanzania: A Case Study of Muhimbili and Ocean Road Hospitals

2021 ◽  
Author(s):  
Rahibu A. Abassi ◽  
Amina S. Msengwa ◽  
Rocky R. J. Akarro
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Missing Data ◽  
Binary Logistic Regression ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Imputation Methods ◽  
Multiple Imputations ◽  
Predictive Mean Matching ◽  
Mean Square Errors

Abstract Background Clinical data are at risk of having missing or incomplete values for several reasons including patients’ failure to attend clinical measurements, wrong interpretations of measurements, and measurement recorder’s defects. Missing data can significantly affect the analysis and results might be doubtful due to bias caused by omission of missed observation during statistical analysis especially if a dataset is considerably small. The objective of this study is to compare several imputation methods in terms of efficiency in filling-in the missing data so as to increase the prediction and classification accuracy in breast cancer dataset. Methods Five imputation methods namely series mean, k-nearest neighbour, hot deck, predictive mean matching, and multiple imputations were applied to replace the missing values to the real breast cancer dataset. The efficiency of imputation methods was compared by using the Root Mean Square Errors and Mean Absolute Errors to obtain a suitable complete dataset. Binary logistic regression and linear discrimination classifiers were applied to the imputed dataset to compare their efficacy on classification and discrimination. Results The evaluation of imputation methods revealed that the predictive mean matching method was better off compared to other imputation methods. In addition, the binary logistic regression and linear discriminant analyses yield almost similar values on overall classification rates, sensitivity and specificity. Conclusion The predictive mean matching imputation showed higher accuracy in estimating and replacing missing/incomplete data values in a real breast cancer dataset under the study. It is a more effective and good method to handle missing data in this scenario. We recommend to replace missing data by using predictive mean matching since it is a plausible approach toward multiple imputations for numerical variables, as it improves estimation and prediction accuracy over the use complete-case analysis especially when percentage of missing data is not very small.

Sign in / Sign up

Export Citation Format

Share Document