scholarly journals A Comparison Study of Goodness of Fit Tests of Logistic Regression in R: Simulation and Application to Breast Cancer Data

2020 ◽  
pp. 50-59
Author(s):  
El-Housainy A. Rady ◽  
Mohamed R. Abonazel ◽  
Mariam H. Metawe’e
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Sample Size ◽  
Null Hypothesis ◽  
Goodness Of Fit ◽  
Quadratic Term ◽  
Breast Cancer Dataset ◽  
Cancer Data ◽  
Interaction Term ◽  
Test Package

Goodness of fit (GOF) tests of logistic regression attempt to find out the suitability of the model to the data. The null hypothesis of all GOF tests is the model fit. R as a free software package has many GOF tests in different packages. A Monte Carlo simulation has been conducted to study two situations; the first, studying the ability of each test, under its default settings, to accept the null hypothesis when the model truly fitted. The second, studying the power of these tests when assumptions of sufficient linear combination of the explanatory variables are violated (by omitting linear covariate term, quadratic term, or interaction term). Moreover, checking whether the same test in different R packages had the same results or not. As the sample size supposed to affect simulation results, so the pattern of change of GOF tests results under different sample sizes as well as different model settings was estimated. All tests accept the null hypothesis (more than 95% of simulation trials) when the model truly fitted except modified Hosmer-Lemeshow test in "LogisticDx" package under all different model settings and Osius and Rojek’s (OsRo) test when the true model had an interaction term between binary and categorical covariates. In addition, le Cessie-van Houwelingen-Copas-Hosmer unweighted sum of squares (CHCH) test gave unexpected different results under different packages. Concerning the power study, all tests had a very low power when a departure of missing covariate existed. Generally, stukel’s test (package ’LogisticDX) and CHCH test (package "RMS") reached a power in detecting a missing quadratic term greater than 80% under lower sample size while OsRo test (package ’LogisticDX’) was better in detecting missing interaction term. Beside the simulation study, we evaluated the performance of GOF tests using the breast cancer dataset.

scholarly journals Predicting Breast Cancer Using Logistic Regression and Multi-Class Classifiers

2018 ◽  
Vol 7 (4.20) ◽  
pp. 22 ◽  
Author(s):  
Jabeen Sultana ◽  
Abdul Khader Jilani ◽  
. .
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Regression Method ◽  
Breast Cancer Dataset ◽  
Breast Cancer Data ◽  
Data Set ◽  
Cancer Data ◽  
Logistic Regression Method ◽  
Simple Logistic

The primary identification and prediction of type of the cancer ought to develop a compulsion in cancer study, in order to assist and supervise the patients. The significance of classifying cancer patients into high or low risk clusters needs commanded many investigation teams, from the biomedical and the bioinformatics area, to learn and analyze the application of machine learning (ML) approaches. Logistic Regression method and Multi-classifiers has been proposed to predict the breast cancer. To produce deep predictions in a new environment on the breast cancer data. This paper explores the different data mining approaches using Classification which can be applied on Breast Cancer data to build deep predictions. Besides this, this study predicts the best Model yielding high performance by evaluating dataset on various classifiers. In this paper Breast cancer dataset is collected from the UCI machine learning repository has 569 instances with 31 attributes. Data set is pre-processed first and fed to various classifiers like Simple Logistic-regression method, IBK, K-star, Multi-Layer Perceptron (MLP), Random Forest, Decision table, Decision Trees (DT), PART, Multi-Class Classifiers and REP Tree.  10-fold cross validation is applied, training is performed so that new Models are developed and tested. The results obtained are evaluated on various parameters like Accuracy, RMSE Error, Sensitivity, Specificity, F-Measure, ROC Curve Area and Kappa statistic and time taken to build the model. Result analysis reveals that among all the classifiers Simple Logistic Regression yields the deep predictions and obtains the best model yielding high and accurate results followed by other methods IBK: Nearest Neighbor Classifier, K-Star: instance-based Classifier, MLP- Neural network. Other Methods obtained less accuracy in comparison with Logistic regression method.  

Design of novel multi filter union feature selection framework for breast cancer dataset

2021 ◽  
pp. 1063293X2110160
Author(s):  
Dinesh Morkonda Gunasekaran ◽  
Prabha Dhandayudam
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Care Center ◽  
Feature Selection Method ◽  
Selection Method ◽  
Cancer Center ◽  
Breast Cancer Dataset ◽  
Data Set ◽  
Health Care Center ◽  
Cancer Data

Nowadays women are commonly diagnosed with breast cancer. Feature based Selection method plays an important step while constructing a classification based framework. We have proposed Multi filter union (MFU) feature selection method for breast cancer data set. The feature selection process based on random forest algorithm and Logistic regression (LG) algorithm based union model is used for selecting important features in the dataset. The performance of the data analysis is evaluated using optimal features subset from selected dataset. The experiments are computed with data set of Wisconsin diagnostic breast cancer center and next the real data set from women health care center. The result of the proposed approach shows high performance and efficient when comparing with existing feature selection algorithms.

scholarly journals Identification of Bio-Markers for Breast Cancer Detection through Data Mining Methods

2019 ◽  
Vol 8 (2S3) ◽  
pp. 763-769
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Feature Selection ◽  
Discriminant Analysis ◽  
Classification Tree ◽  
Partial Least Square ◽  
Diagnostic Methods ◽  
Support Vector ◽  
Breast Cancer Dataset

Worldwide, breast cancer is the leading type of cancer in women accounting for 25% of all cases. Survival rates in the developed countries are comparatively higher with that of developing countries. This had led to the importance of computer aided diagnostic methods for early detection of breast cancer disease. This eventually reduces the death rate. This paper intents the scope of the biomarker that can be used to predict the breast cancer from the anthropometric data. This experimental study aims at computing and comparing various classification models (Binary Logistic Regression, Ball Vector Machine (BVM), C4.5, Partial Least Square (PLS) for Classification, Classification Tree, Cost sensitive Classification Tree, Cost sensitive Decision Tree, Support Vector Machine for Classification, Core Vector Machine, ID3, K-Nearest Neighbor, Linear Discriminant Analysis (LDA), Log-Reg TRIRLS, Multi Layer Perceptron (MLP), Multinomial Logistic Regression (MLR), Naïve Bayes (NB), PLS for Discriminant Analysis, PLS for LDA, Random Tree (RT), Support Vector Machine SVM) for the UCI Coimbra breast cancer dataset. The feature selection algorithms (Backward Logit, Fisher Filtering, Forward Logit, ReleifF, Step disc) are worked out to find out the minimum attributes that can achieve a better accuracy. To ascertain the accuracy results, the Jack-knife cross validation method for the algorithms is conducted and validated. The Core vector machine classification algorithm outperforms the other nineteen algorithms with an accuracy of 82.76%, sensitivity of 76.92% and specificity of 87.50% for the selected three attributes, Age, Glucose and Resistin using ReleifF feature selection algorithm.

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.

scholarly journals Ensemble Comparative Study for Diagnosis of Breast Cancer Datasets

2018 ◽  
Vol 7 (4.15) ◽  
pp. 281
Author(s):  
Bibhuprasad Sahu ◽  
Sujata Dash ◽  
Sachi Nandan Mohanty ◽  
Saroj Kumar Rout
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Early Stage ◽  
Breast Cancer Dataset ◽  
Classification Rate ◽  
Cancer Dataset ◽  
Cancer Data ◽  
Cad System ◽  
Result Analysis ◽  
Sensitivity Specificity

Every disease is curable if a little amount of human effort is applied for early diagnosis. The death rate in world increases day by day as patient fail to detect it before it becomes chronic. Breast cancer is curable if detection is done at early stage before it spread across all part of body. Now-a-days computer aided diagnosis are automated assistance for the doctors to produce accurate prediction about the stage of disease. This study provided CAD system for diagnosis of breast cancer. This method uses Neural Network (NN) as a classifier model and PCA/LDA for dimension reduction method to attain higher classification rate. Multiple layers of neural network are applied to classify the breast cancer data. This system experiment done on Wisconsin breast cancer dataset (WBCD) from UCI repository. The dataset is divided into 2 parts train and test. With the result of accuracy, sensitivity, specificity, precision and recall the performance can be measured. The results obtained are this study is 97% using ANN and PCA-ANN, which is better than other state-of-art methods. As per the result analysis this system outperformed then the existing system.  

scholarly journals Synthesising artificial patient-level data for Open Science - an evaluation of five methods

2020 ◽  
Author(s):  
Michael Allen ◽  
Andrew Salmon
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Synthetic Data ◽  
Original Data ◽  
Classification Model ◽  
Data Sets ◽  
List Type ◽  
Breast Cancer Data ◽  
Data Set ◽  
Cancer Data

ABSTRACTBackgroundOpen science is a movement seeking to make scientific research accessible to all, including publication of code and data. Publishing patient-level data may, however, compromise the confidentiality of that data if there is any significant risk that data may later be associated with individuals. Use of synthetic data offers the potential to be able to release data that may be used to evaluate methods or perform preliminary research without risk to patient confidentiality.MethodsWe have tested five synthetic data methods:A technique based on Principal Component Analysis (PCA) which samples data from distributions derived from the transformed data.Synthetic Minority Oversampling Technique, SMOTE which is based on interpolation between near neighbours.Generative Adversarial Network, GAN, an artificial neural network approach with competing networks - a discriminator network trained to distinguish between synthetic and real data., and a generator network trained to produce data that can fool the discriminator network.CT-GAN, a refinement of GANs specifically for the production of structured tabular synthetic data.Variational Auto Encoders, VAE, a method of encoding data in a reduced number of dimensions, and sampling from distributions based on the encoded dimensions.Two data sets are used to evaluate the methods:The Wisconsin Breast Cancer data set, a histology data set where all features are continuous variables.A stroke thrombolysis pathway data set, a data set describing characteristics for patients where a decision is made whether to treat with clot-busting medication. Features are mostly categorical, binary, or integers.Methods are evaluated in three ways:The ability of synthetic data to train a logistic regression classification model.A comparison of means and standard deviations between original and synthetic data.A comparison of covariance between features in the original and synthetic data.ResultsUsing the Wisconsin Breast Cancer data set, the original data gave 98% accuracy in a logistic regression classification model. Synthetic data sets gave between 93% and 99% accuracy. Performance (best to worst) was SMOTE > PCA > GAN > CT-GAN = VAE. All methods produced a high accuracy in reproducing original data means and stabdard deviations (all R-square > 0.96 for all methods and data classes). CT-GAN and VAE suffered a significant loss of covariance between features in the synthetic data sets.Using the Stroke Pathway data set, the original data gave 82% accuracy in a logistic regression classification model. Synthetic data sets gave between 66% and 82% accuracy. Performance (best to worst) was SMOTE > PCA > CT-GAN > GAN > VAE. CT-GAN and VAE suffered loss of covariance between features in the synthetic data sets, though less pronounced than with the Wisconsin Breast Cancer data set.ConclusionsThe pilot work described here shows, as proof of concept, that synthetic data may be produced, which is of sufficient quality to publish with open methodology, to allow people to better understand and test methodology. The quality of the synthetic data also gives promise of data sets that may be used for screening of ideas, or for research project (perhaps especially in an education setting).More work is required to further refine and test methods across a broader range of patient-level data sets.

scholarly journals Breast cancer prediction model with decision tree and adaptive boosting

2021 ◽  
Vol 10 (1) ◽  
pp. 184
Author(s):  
Tsehay Admassu Assegie ◽  
R. Lakshmi Tulasi ◽  
N. Komal Kumar
Keyword(s):  
Breast Cancer ◽  
Decision Tree ◽  
Prediction Model ◽  
Predictive Performance ◽  
Data Repository ◽  
Breast Cancer Dataset ◽  
Adaptive Boosting ◽  
Cancer Data ◽  
Adaboost Algorithm ◽  
Cancer Prediction

In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased to the benign observation and results in poor performance on predicting the malignant observation. To improve the performance of the decision tree on the malignant observation, boosting algorithm namely, the adaptive boosting is employed. Finally, the predictive performance of the decision tree and adaptive boosting is analyzed. The analysis on predictive performance of the model on the kaggle breast cancer data repository shows that, adaptive boosting has 92.53% accuracy and the accuracy of decision tree is 88.80%, Overall, the adaboost algorithm performed better than decision tree.

scholarly journals Evaluation of Modified Categorical Data Fuzzy Clustering Algorithm on the Wisconsin Breast Cancer Dataset

Scientifica ◽  
2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Amir Ahmad
Keyword(s):  
Breast Cancer ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Distance Measure ◽  
Hamming Distance ◽  
Machine Learning Techniques ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Cancer Data ◽  
Feature Values

The early diagnosis of breast cancer is an important step in a fight against the disease. Machine learning techniques have shown promise in improving our understanding of the disease. As medical datasets consist of data points which cannot be precisely assigned to a class, fuzzy methods have been useful for studying of these datasets. Sometimes breast cancer datasets are described by categorical features. Many fuzzy clustering algorithms have been developed for categorical datasets. However, in most of these methods Hamming distance is used to define the distance between the two categorical feature values. In this paper, we use a probabilistic distance measure for the distance computation among a pair of categorical feature values. Experiments demonstrate that the distance measure performs better than Hamming distance for Wisconsin breast cancer data.

scholarly journals Image Descriptors for Weakly Annotated Histopathological Breast Cancer Data

2020 ◽  
Vol 2 ◽  
Author(s):  
Panagiotis Stanitsas ◽  
Anoop Cherian ◽  
Vassilios Morellas ◽  
Resha Tejpaul ◽  
Nikolaos Papanikolopoulos ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computer Vision ◽  
State Of The Art ◽  
Area Under The Curve ◽  
Multiple Instance Learning ◽  
Superior Performance ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Cancer Data ◽  
Learning Framework

Introduction: Cancerous Tissue Recognition (CTR) methodologies are continuously integrating advancements at the forefront of machine learning and computer vision, providing a variety of inference schemes for histopathological data. Histopathological data, in most cases, come in the form of high-resolution images, and thus methodologies operating at the patch level are more computationally attractive. Such methodologies capitalize on pixel level annotations (tissue delineations) from expert pathologists, which are then used to derive labels at the patch level. In this work, we envision a digital connected health system that augments the capabilities of the clinicians by providing powerful feature descriptors that may describe malignant regions.Material and Methods: We start with a patch level descriptor, termed Covariance-Kernel Descriptor (CKD), capable of compactly describing tissue architectures associated with carcinomas. To leverage the recognition capability of the CKDs to larger slide regions, we resort to a multiple instance learning framework. In that direction, we derive the Weakly Annotated Image Descriptor (WAID) as the parameters of classifier decision boundaries in a Multiple Instance Learning framework. The WAID is computed on bags of patches corresponding to larger image regions for which binary labels (malignant vs. benign) are provided, thus obviating the necessity for tissue delineations.Results: The CKD was seen to outperform all the considered descriptors, reaching classification accuracy (ACC) of 92.83%. and area under the curve (AUC) of 0.98. The CKD captures higher order correlations between features and was shown to achieve superior performance against a large collection of computer vision features on a private breast cancer dataset. The WAID outperform all other descriptors on the Breast Cancer Histopathological database (BreakHis) where correctly classified malignant (CCM) instances reached 91.27 and 92.00% at the patient and image level, respectively, without resorting to a deep learning scheme achieves state-of-the-art performance.Discussion: Our proposed derivation of the CKD and WAID can help medical experts accomplish their work accurately and faster than the current state-of-the-art.

scholarly journals Using Simple Alternative Hypothesis to Increase Statistical Power in Sparse Categorical Data

2017 ◽  
Vol 6 (6) ◽  
pp. 158
Author(s):  
Louis Mutter ◽  
Steven B. Kim
Keyword(s):  
Sample Size ◽  
Categorical Data ◽  
Null Hypothesis ◽  
Statistical Power ◽  
Goodness Of Fit ◽  
Alternative Hypothesis ◽  
Small Sample ◽  
Statistical Hypothesis ◽  
Hypothesis Tests ◽  
Simple Alternative

There are numerous statistical hypothesis tests for categorical data including Pearson's Chi-Square goodness-of-fit test and other discrete versions of goodness-of-fit tests. For these hypothesis tests, the null hypothesis is simple, and the alternative hypothesis is composite which negates the simple null hypothesis. For power calculation, a researcher specifies a significance level, a sample size, a simple null hypothesis, and a simple alternative hypothesis. In practice, there are cases when an experienced researcher has deep and broad scientific knowledge, but the researcher may suffer from a lack of statistical power due to a small sample size being available. In such a case, we may formulate hypothesis testing based on a simple alternative hypothesis instead of the composite alternative hypothesis. In this article, we investigate how much statistical power can be gained via a correctly specified simple alternative hypothesis and how much statistical power can be lost under a misspecified alternative hypothesis, particularly when an available sample size is small.

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