Application of Data Mining for high accuracy prediction of breast tissue biopsy results

Kaushik, Divyansh; Kaur, Karamjit

doi:10.1109/dipdmwc.2016.7529361

Cited by 18 publications

(8 citation statements)

References 10 publications

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“…The LR classification method [ 27 ] is a popular choice for modeling binary classifications. Linearly combining the input characteristics is thought to make one of the two output classes more likely than the other one [ 28 ]. This classification model's logistic equation is

where P denotes the probability of the incidence of event a…”

Section: Methodsmentioning

confidence: 99%

Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches

Khan

Tazin

Hussain

et al. 2022

Computational Intelligence and Neuroscience

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Breast cancer develops when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue known as a tumor. This lump of tissue is called a tumor. After skin cancer, breast cancer is the second most common cancer among women. It is more common in women over the age of 50. Men may also acquire breast cancer, albeit it is uncommon. Each year, approximately 2,600 men in the United States are diagnosed with breast cancer, accounting for less than 1% of all cases. Transgender women are more likely than cisgender men to acquire breast cancer. Additionally, transgender males are less likely than cisgender women to acquire breast cancer. Breast cancer is more common in women over the age of 50, although it can affect anyone at any age. Early detection of a breast tumor may significantly lower the risk of developing breast cancer. A public dataset of breast tumor features was used instead to build models for identifying breast tumors through machine learning and deep learning. Prediction models were built using logistic regression (LR), decision tree (DT), random forest (RF), voting classifier (VC), support vector machine (SVM), and a proprietary convolutional neural network (CNN). These models were used to find critical prognostic indicators linked to breast cancer. The proposed network performs far better, with an average accuracy of 99%. This study has six types of models: LR, RF, SVM, VC, DT, and a custom CNN model. They all had 96% to 99% accuracy in this study. CNN, LR, RF, SVM, VC, and DT achieved 99%, 96%, 98%, 97%, 97%, and 96% F1 score, respectively. There were many machine learning algorithms used in this study that were very accurate, which means that these techniques could be used as alternative prognostic tools in breast tumor detection studies in Asia.

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where P denotes the probability of the incidence of event a…”

Section: Methodsmentioning

confidence: 99%

Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches

Khan

Tazin

Hussain

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…The researchers used naive Bayesian, random forest, and SVM classifiers. Another study by Kaushik et al [38] utilized data mining techniques for the prediction of breast tissue biopsy results. The researchers proposed a model with an accuracy of 83.5% and a ROC of 0.907.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, the dataset was obtained specific registry dataset format. Kaushik et al [38] used naïve Bayesian, RBF network, and decision tree techniques to predict breast cancer on Wisconsin dataset provided by the University of California Irvine machine learning repository [39]. They achieved the best accuracy of 97.36% related to the naïve Bayesian classifier.…”

Section: Discussionmentioning

confidence: 99%

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

Sohrabei

Atashi

2021

Front Health Inform

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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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“…It is widely used in applications like critical thinking, media distribution industry, retail, genetic data analysis, financial data, logical applications, health mind frameworks, etc [9]. Using Data Mining, we can not only substantially reduce this amount, as well as save doctors and patients' time and resources [10]. Due to the enormous data set size [11], it is important to provide faster and more costeffective models while using data mining techniques to carry out the classification of ECG datasets.…”

Section: Introductionmentioning

confidence: 99%

Efficient Data-Mining Classification Approach For Ecg Data In Health Care Application

Shana¹,

Venkatachalam²

2021

Preprint

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Data mining enables classification of Electrocardiographic (ECG) signals of the heart for diagnosing many cardiac diseases. ECG signals often consist of unwanted noises, speckles and redundant features. An unwanted noise and redundant features always degrade the quality of ECG signal and may lead to loss of accuracy in classification technique. To overcome these challenges, we introduced Optimize Discrete Kernel Vector (ODKV) classifier with an impressive pre-processing in this paper. In order to remove the noises, image processing filter namely the Adaptive Notch Filters (ANF) are initially used to remove Power Line Interference from ECG Signals. Moreover, reducing the redundant features from the ECG signal plays a vital role in diagnosing the cardiac disease. So, Optimize Discrete Kernel Vector (ODKV) classifier is used to reduce the redundant features and also to enhance the classification accuracy of the input ECG signal. Thus, Optimize Discrete Kernel Vector (ODKV) classifier identifies the Q wave, R wave and S wave in the input ECG signal. Finally, performance metrics Sensitivity, Specificity, Accuracy and Mean Square Error (MSE) are calculated and compared with the existing method such as SVM-kNN, ANN-kNN, GB-SVNN, and CNN to prove the enhancement of the classification technique.

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Application of Data Mining for high accuracy prediction of breast tissue biopsy results

Cited by 18 publications

References 10 publications

Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches

Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches

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

Efficient Data-Mining Classification Approach For Ecg Data In Health Care Application

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