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.

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.

The Prediction of River Water Pollution Density Based on Data Mining Technology

2010 ◽  
Vol 113-116 ◽  
pp. 1285-1288
Author(s):  
Chen Ye Wang ◽  
Bin Sheng Liu ◽  
Er Wei Qiu
Keyword(s):  
Neural Network ◽  
Water Pollution ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Rough Set Theory ◽  
Mining Technology ◽  
Bp Artificial Neural Network ◽  
Three Stages ◽  
Artificial Neural ◽  
Using Data

In order to increase the prediction precision, this article proposes a forecasting model in water pollution density based on data mining technology. The model consists of three stages: first, the rough set theory and the genetic algorithm are applied to select relevant forecasting variable to the water pollution density; second, training pattern of artificial neural network which is similar to the forecast term is carried out by using data mining technology; finally the artificial neural network is used to carry on forecasting the water pollution density. The applied result shows that this model has a higher precision and surpasses gray GM (1, 1) and the pure BP artificial neural network model.

scholarly journals Data Mining using Artificial Neural Network POS_NEG Rules

2013 ◽  
Vol 11 (6) ◽  
pp. 2709-2714
Author(s):  
Pushkar Shinde ◽  
Dr. Varsha Patil
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Backpropagation Algorithm ◽  
The Neural Network ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann ◽  
Sensitivity Method

Diabetes patients are increasing in number so it is necessary to predict , treat and diagnose the disease. Data Mining can help to provide knowledge about this disease. The knowledge extracted using Data Mining can help in treating and preventing the disease. Artificial Neural Network (ANN) can be used to create an classifier from the data. The neural network is trained using backpropagation algorithm The knowledge stored in the neural network is used to predict the disease. The knowledge stored in neural network is extracted using Pos-Neg sensitivity method. The knowledge extracted is in form of sensitivity analysis to analyze the disease and in turn help in treating the disease.

scholarly journals Performance Analysis of Data Mining Techniques for the Prediction Breast Cancer Risk on Big Data

2021 ◽  
Vol 10 (1) ◽  
pp. 83
Author(s):  
Solmaz Sohrabei ◽  
Alireza Atashi
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Risk Factors ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Early Detection ◽  
Breast Cancers ◽  
Data Mining Techniques ◽  
Naive Bayesian ◽  
Naïve Bayesian

Introduction: Early detection breast cancer Causes it most curable cancer in among other types of cancer, early detection and accurate examination for breast cancer ensures an extended survival rate of the patients. Risk factors are an important parameter in breast cancer has an important effect on breast cancer. Data mining techniques have a growing reputation in the medical field because of high predictive capability and useful classification. These methods can help practitioners to develop tools that allow detecting the early stages of breast cancer.Material and Methods: The database used in this paper is provided by Motamed Cancer Institute, ACECR Tehran, Iran. It contains of 7834 records of breast cancer patients clinical and risk factors data. There were 4008 patients (52.4%) with breast cancers (malignant) and the remaining 3617 patients (47.6%) without breast cancers (benign). Support vector machine, multi-layer perceptron, decision tree, K nearest neighbor, random forest, naïve Bayesian models were developed using 20 fields (risk factor) of the database because database feature was restrictions. Used 10-fold crossover for models evaluate. Ultimately, the comparison of the models was made based on sensitivity, specificity and accuracy indicators.Results: Naïve Bayesian and artificial neural network are better models for the prediction of breast cancer risks. Naïve Bayesian had accuracy of 93%, specificity of 93.32%, sensitivity of 95056%, ROC of 0.95 and artificial neural network had accuracy of 93.23%, specificity of 91.98%, sensitivity of 92.69%, and ROC of 0.8.Conclusion: Strangely the different artificial intelligent calculations utilized in this examination yielded close precision subsequently these techniques could be utilized as option prescient instruments in the bosom malignancy risk considers. The significant prognostic components affecting risk pace of bosom disease distinguished in this investigation, which were approved by risk, are helpful and could be converted into choice help devices in the clinical area.

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