Current Developments in the Analysis of Proteomic Data: Artificial Neural Network Data Mining Techniques for the Identification of Proteomic Biomarkers Related to Breast Cancer

2005 ◽  
Vol 2 (1) ◽  
pp. 15-29 ◽  
Author(s):  
Lee Lancashire ◽  
Shahid Mian ◽  
Ian Ellis ◽  
Robert Rees ◽  
Graham Ball
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Network Data ◽  
Data Mining Techniques ◽  
Proteomic Data ◽  
Artificial Neural

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 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.

scholarly journals An Artificial Neural Network Model for Estimating Daily Solar Radiation in Northwest Nigeria

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Salisu Aliyu ◽  
Aminu S Zakari ◽  
Muhammad Ismail ◽  
Mohammed A Ahmed
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Solar Energy ◽  
Network Data ◽  
Radiation Data ◽  
Testing Dataset ◽  
The World ◽  
Artificial Neural

Solar energy has attracted enormous attention as it plays an essential role in meeting the ever growing sustainable and environmental friendly energy demand of the world. Due to the high cost of calibration and maintenance of designated measuring instruments, solar radiation data are limited not only in Nigeria but in most parts of the world. The optimal design of solar energy systems requires accurate estimation of solar radiation. Existing studies are focused on the analysis of monthly or annual solar radiation with less attention paid to the determination of daily solar radiation. Estimating daily solar radiation envisages high quality and performance of solar systems. In this paper, an Artificial Neural Network data mining model is proposed for estimating the daily solar radiation in Kano, Kaduna and Katsina, North West Nigeria. Daily Solar radiation data for 21years collected from the Nigerian Metrological Agency were used as training/testing data while developing the model. Two statistical indicators: coefficient of determination (R2) and the root mean square error (RMSE) were used to evaluate the model. An RMSE of 0.47 and 0.48 was obtained for the training and testing dataset respectively, while an R2 of 0.78 was obtained for both the training and testing dataset. The overall results showed that artificial neural network exhibits excellent performance for the estimation of daily solar radiation.Keywords— Artificial Neural Network, Data mining, Solar Radiation 

scholarly journals Data Mining Techniques in Artificial Neural Network for UWB Antenna Design

2018 ◽  
Vol 27 (1) ◽  
pp. 70-78
Author(s):  
Li-Ye Xiao ◽  
Wei Shao ◽  
Zhi-Xin Yao ◽  
Shanshan Gao
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Antenna Design ◽  
Uwb Antenna ◽  
Data Mining Techniques ◽  
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.

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