scholarly journals Estimating Breast Cancer Class Using Artificial Neural Network and Logistic Regression Methods

2022 ◽  
Author(s):  
Serel ÖZMEN-AKYOL
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Regression Methods ◽  
Artificial Neural

Logistic Regression and Artificial Neural Network: A Comparative Study in Diagnosing Breast Cancer

2021 ◽  
pp. 6330-6334
Author(s):  
W. Abdul Hameed ◽  
Anuradha D. ◽  
Kaspar S.
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Breast Tumor ◽  
Diagnostic Performance ◽  
Malignant Breast Tumor ◽  
Malignant Breast ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Breast tumor is a common problem in gynecology. A reliable test for preoperative discrimination between benign and malignant breast tumor is highly helpful for clinicians in culling the malignant cells through felicitous treatment for patients. This paper is carried out to generate and estimate both logistic regression technique and Artificial Neural Network (ANN) technique to predict the malignancy of breast tumor, utilizing Wisconsin Diagnosis Breast Cancer Database (WDBC). Our aim in this Paper is: (i) to compare the diagnostic performance of both methods in distinguishing between malignant and benign patterns, (ii) to truncate the number of benign cases sent for biopsy utilizing the best model as an auxiliary implement, and (iii) to authenticate the capability of each model to recognize incipient cases as an expert system.

scholarly journals Predicting Breast Cancer Survivability

2020 ◽  
Vol 4 (1) ◽  
pp. 17-30
Author(s):  
Omead I. Hussain
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Decision Trees ◽  
Modeling Tools ◽  
Data Mining Techniques ◽  
Artificial Neural ◽  
Using Data

this study concentrates on Predicting Breast Cancer Survivability using data mining, and comparing between three main predictive modeling tools. Precisely, we used three popular data mining methods: two from machine learning (artificial neural network and decision trees) and one from statistics (logistic regression), and aimed to choose the best model through the efficiency of each model and with the most effective variables to these models and the most common important predictor. We defined the three main modeling aims and uses by demonstrating the purpose of the modeling. By using data mining, we can begin to characterize and describe trends and patterns that reside in data and information. The preprocessed data set contents were of 87 variables and the total of the records are 457,389; which became 93 variables and 90308 records for each variable, and these dataset were from the SEER database. We have achieved more than three data mining techniques and we have investigated all the data mining techniques and finally we find the best thing to do is to focus about these data mining techniques which are Artificial Neural Network, Decision Trees and Logistic Regression by using SAS Enterprise Miner 5.2 which is in our view of point is the suitable system to use according to the facilities and the results given to us. Several experiments have been conducted using these algorithms. The achieved prediction implementations are Comparison-based techniques. However, we have found out that the neural network has a much better performance than the other two techniques. Finally, we can say that the model we chose has the highest accuracy which specialists in the breast cancer field can use and depend on.

scholarly journals Prediction of the Pollutants Concentration Using Artificial Neural Network (ANN)

2015 ◽  
Vol 16 (SE) ◽  
pp. 171-180
Author(s):  
Ahmad Mousavian ◽  
Hady Zarei Mahmodabady ◽  
Aboutaleb Ghadami Jadval Ghadam
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Artificial Neural Network ◽  
Air Pollutants ◽  
Environmental Issues ◽  
Urban Transport ◽  
Regression Methods ◽  
Tangent Function ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Air pollution is one of the most important environmental issues that annual causes to mortality large number of people around the world. So, investigating, measuring, and predicting the concentrations of different pollutants in various areas play an important role in preventing the production of this pollutant sand planning to reduce them by people and relevant authorities. One of the new models that play an important role in measuring and predicting pollution is artificial neural network or regression methods. Therefore, this study is trying to predict air pollution in Yasouj by using artificial neural network in 2014. Because the evidences showed that Yasouj due to uncontrolled growth of industrial and urban transport is subject to various air pollutants such as carbon monoxide and particulate matter. Overall, the results of the assessment and prediction of concentration of pollutants of Yasouj by artificial neural network showed that sigmoid transfer function to the hyperbolic tangent function is more efficient in measuring the concentration of pollutants.  

scholarly journals P055 THE USE OF ARTIFICIAL NEURAL NETWORK TO PREDICT THE RISK OF INCISIONAL HERNIA AFTER MIDLINE LAPAROTOMY

2021 ◽  
Vol 108 (Supplement_8) ◽  
Author(s):  
Edgard Efren Lozada Hernandez ◽  
Tania Aglae Ramírez del Real ◽  
Dagoberto Armenta Medina ◽  
Jose Francisco Molina Rodriguez ◽  
Juan ramon Varela Reynoso
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Incisional Hernia ◽  
False Negative ◽  
Wound Dehiscence ◽  
Midline Laparotomy ◽  
Significant Difference ◽  
Positive Rate ◽  
Artificial Neural

Abstract Aim “Incisional Hernia (IH) has an incidence of 10-23%, which can increase to 38% in specific risk groups. The objective of this study was developed and validated an artificial neural network (ANN) model for the prediction of IH after midline laparotomy (ML) and this model can be used by surgeons to help judge a patient’s risk for IH.” Material and Methods “A retrospective, single arm, observational cohort trial was conducted from January 2016 to December 2020. Study participants were recruited from patients undergoing ML for elective or urgent surgical indication. Using logistic regression and ANN models, we evaluated surgical treated IH, wound dehiscence, morbidity, readmission, and mortality using the area under the receiver operating characteristic curves, true-positive rate, true-negative rate, false-positive rate, and false-negative rates.” Results “There was no significant difference in the power of the ANN and logistic regression for predicting IH, wound dehiscence, mortality, readmission, and all morbidities after ML. The resulting model consisted of 4 variables: surgical site infection, emergency surgery, previous laparotomy, and BMI(Kg/m2) > 26. The patient with the four positive factors has a 73% risk of developing incisional hernia. The area under the curve was 0.82 (95% IC 0.76-0.87). Conclusions “ANNs perform comparably to logistic regression models in the prediction of IH. ANNs may be a useful tool in risk factor analysis of IH and clinical applications.”

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