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.

Determination Efficient Classification Algorithm for Credit Card Owners: Comparative Study

2021 ◽  
Vol 39 (1B) ◽  
pp. 21-29
Author(s):  
Raghad A. Azeez
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Financial Institution ◽  
Classification Systems ◽  
Detection Rates ◽  
Business World ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Artificial Neural

Today in the business world, significant loss can happen when the borrowers ignore paying their loans. Convenient credit-risk management represents a necessity for lending institutions. In most times, some persons prefer to late their monthly payments, otherwise, they may face difficulties in the loan payment process to the financial institution. Mainly, most fiscal organizations are considered managed and refined client classification systems, scanning a valid client from invalid ones. This paper produces the data mining idea, specifically the classification technique of data mining and builds a system of data mining process structure. The credit scoring problem will be applied using the Taiwan bank dataset. Besides that, three classification methods are adopted, Naïve Bayesian, Decision Tree (C5.0), and Artificial Neural Network. These classifiers are implemented in the WEKA machine learning application. The results show that the C5.0 algorithm is the best among them, it achieves 0.93 accuracy rates, 0.94 detection rates, 0.96 precision rates, and 0.95 F-Measure which is higher than Naïve Bayesian and Artificial Neural Network; also, the False Positive Rate in C5.0 algorithm achieves 0.1 which is less than Artificial Neural Network and Naïve Bayesian.

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.

Classification Methods in the Detection of New Suspicious Emails

2008 ◽  
Vol 07 (03) ◽  
pp. 209-217 ◽  
Author(s):  
S. Appavu Alias Balamurugan ◽  
G. Athiappan ◽  
M. Muthu Pandian ◽  
R. Rajaram
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Binary Tree ◽  
Experimental Results ◽  
Classification Methods ◽  
Detection Rates ◽  
Naive Bayesian ◽  
The Past ◽  
Naïve Bayesian

Email has become one of the fastest and most economical forms of communication. However, the increase of email users has resulted in the dramatic increase of suspicious emails during the past few years. This paper proposes to apply classification data mining for the task of suspicious email detection based on deception theory. In this paper, email data was classified using four different classifiers (Neural Network, SVM, Naïve Bayesian and Decision Tree). The experiment was performed using weka on the basis of different data size by which the suspicious emails are detected from the email corpus. Experimental results show that simple ID3 classifier which make a binary tree, will give a promising detection rates.

scholarly journals Awareness of Breast Cancer and its Early Detection Measures among Female Students, Northern Ethiopia

2016 ◽  
Vol 5 (3) ◽  
pp. 213
Author(s):  
Tesfay Hailu ◽  
Hailemariam Berhe ◽  
Desta Hailu
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Early Detection ◽  
Sampling Technique ◽  
Knowledge Score ◽  
Female Students ◽  
Warning Signs ◽  
Northern Ethiopia ◽  
Breast Cancers ◽  
Cross Sectional

Globally breast cancer is the most common of all cancers. Since risk reduction strategies cannot eliminate the majority of breast cancers, early detection remains the cornerstone of breast cancer control. This paper, therefore, attempts to assess the awareness of breast cancer and its early detection measures among female students in Mekelle University, Ethiopia. An institution based cross-sectional study was conducted on randomly selected female students. Multistage sampling technique was employed to select the participants. A pre-tested structured questionnaire was used. Data analysis was carried out using SPSS version 16. In this study, 760 students participated making a response rate of 96 percent. Respondents with good knowledge score for risk factors, early detections measures and warning signs of breast cancer were 1.4 percent, 3.6 percent and 22.1 percent respectively. The majority 477 (62.8 percent) of participants practiced self-breast examination. In conclusion the participants had poor knowledge of risk factors, early detection measures and early warning signs of breast cancer.Therefore, the Ministry of health of Ethiopia together with its stalk holders should strengthen providing IEC targeting women to increase their awareness about breast cancer and its early detection measure.

scholarly journals Knowledge, attitude and practice of Sri Lankan women towards breast cancer: A cross sectional study

2020 ◽  
Author(s):  
N.D. Ranaweera ◽  
P.M.C. Dinesha ◽  
C.A.K. Pathirage ◽  
P.W.D.N. Weerasinghe ◽  
D.M.K.N. Senarathna ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Sri Lanka ◽  
Early Detection ◽  
Signs And Symptoms ◽  
Good Knowledge ◽  
Screening Methods ◽  
Breast Cancers ◽  
Detection Techniques ◽  
Cancer Hospital

Abstract Introduction Breast cancer is a type of cancer that develops from breast tissue. Although the knowledge on breast cancer among women in Sri Lanka is high, their practice of breast self-examination is poor. This study was aimed to determine attitudes, practices and the awareness of early detection techniques and risk factors among women in Sri Lanka. Method A study was conducted between two groups in National Cancer hospital, Sri Lanka and a peripheral women clinic consisting 317 participants in each group. Self-administered questionnaire was used to collect data regarding the awareness of early detection techniques, signs and symptoms and risk factors for breast cancers. Results A total of 33.8% of peripheral women clinic and 65.0% in National Cancer hospital patients had good knowledge regarding risk factors. The knowledge of risk factors, signs and symptoms and screening methods about breast cancers was significantly high in patients attending to National cancer hospital. Older aged people have less awareness about screening methods compared to younger people. People with high education background and those have a family history of breast cancer had more aware about screening and therapeutic methods. Conclusion Comparative to the peripheral women clinic, patients attending to National Cancer Hospital had a fairly good knowledge, practice and attitude regarding the early detection techniques, risk factors, signs and symptoms.

scholarly journals Breast cancer segmentation and classification in ultrasound images using convolutional neural network

2021 ◽  
Author(s):  
Shima Baniadam Dizaj ◽  
Pourya Valizadeh
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Ultrasound Images ◽  
Ultrasound Scan ◽  
Breast Cancers ◽  
The World ◽  
Breast Segmentation

Abstract Breast most cancers is one of the main reasons of mortality in ladies throughout the world. Early detection contributes to a discount withinside the quantity of untimely fatalities. Using ultrasound (US) pics, we gift deep studying (DL) strategies for breast most cancers segmentation and category into 3 classes: regular, benign, and malignant. The versions in most cancers length and traits are the mission of segmentation and category tasks. The proposed technique became evolved and evaluated the use of US pics amassed from 780 breast cancers. This has a look at tested using deep studying to scientific pics of breast most cancers acquired with the aid of using ultrasound scan. For evaluation, we used intersection over union (IoU), accuracy. When evaluated with IoU the nice proposed technique yielded 100%curacy on regular breast segmentation, 79.27% on benign, and 93.73% on malignant most cancers. Also, the accuracy of category three classes is 87.86%. Our have a look at indicates the usefulness of deep studying techniques for breast most cancers segmentation and category. You can locate the preskilled weights and elements of our Implementation and the prediction of our technique may be located at https://github.com/shb8086/Cancer.

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