Prediction of Breast Cancer Using Big Data Analytics

Shailaja, K.; Seetharamulu, B.; Jabbar, M. A.

doi:10.14419/ijet.v7i4.6.20480

Cited by 11 publications

(6 citation statements)

References 1 publication

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“…(2) Determining the number of hidden layers and the number of hidden nodes in the hidden layers depending on the relevant problem area Mostly, an approach in the form of backpropagation is followed (6) Determination of the stopping condition. The learning process can be terminated when all the training data are evaluated on the network or when a desired error rate or a specified time criterion is reached (7) They are comparing the result with the desired result by evaluating each training data in the network until the stopping condition is met. If the result is as desired, adjusting the weights increases the probability of getting the same result when similar data is received.…”

Section: Neural Network-based Classifiersmentioning

confidence: 99%

“…Second, only to lung cancer, breast cancer is the most common cancer globally in terms of incidence. Breast cancer is estimated to affect one in eight women at some point in their lives [7]. Even though men can be affected, women are 100 times more likely than men to be affected.…”

Section: Introductionmentioning

confidence: 99%

“…There is only one way to know whether the mass in the breast is benign (benign) or malignant (malignant). But some features can give an average idea to the examining physician about what that mass looks like [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques

Mohammad

Teete

Al-Aaraj

et al. 2022

Applied Bionics and Biomechanics

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Breast cancer must be addressed by a multidisciplinary team aiming at the patient’s comprehensive treatment. Recent advances in science make it possible to evaluate tumor staging and point out the specific treatment. However, these advances must be combined with the availability of resources and the easy operability of the technique. This study is aimed at distinguishing and classifying benign and malignant cells, which are tumor types, from the data on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by applying data mining classification and clustering techniques with the help of the Weka tool. In addition, various algorithms and techniques used in data mining were measured with success percentages, and the most successful ones on the dataset were determined and compared with each other.

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Section: Neural Network-based Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques

Mohammad

Teete

Al-Aaraj

et al. 2022

Applied Bionics and Biomechanics

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“…Towards increasing the classification accuracy and improving the performance, K nearest neighbour algorithms are being employed. [30] The data being provided for the algorithm consists of data being structured, semi-structured as well as unstructured. This data is efficiently being handled by tools available in big data.…”

Section: Application Of Big Data Analytics In Breast Cancermentioning

confidence: 99%

A Comprehensive Big Data Analytics Based Framework for Premature Recognition of Breast Cancer

Venkatesan*,

Kulkarni,

Menon

et al. 2020

IJITEE

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Healthcare industry is fast growing and expanding in rapid pace. The volume and veracity of data generated in the industry is massive and requires huge storages and handling capability. Big data is empowered with such robust abilities and hence most suitable for handing large amount of data. Further, hese data could be utilized towards building predictive and forecasting models. Breast cancer is a deadly form of cancer majorly affecting women around the globe. The concept of big data and predictive analytics is being explored in the paper towards early diagnosis of breast cancer. This paper surveys various literatures available on application of big data analysis for breast cancer. Subsequently a comprehensive framework is being proposed based on the gaps identified. Different machine learning algorithms which can be applied in the framework is also detailed in the paper. Such frameworks when implemented will greatly help in handling the massive data available and aid in early detection of breast cancer.

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“…The integration of information and communication technologies [9], big data [10,11], cyberphysical system (CPS) [12] and machine learning (ML) can help in diagnosis and prognosis of breast cancer. Big data is referred to as a large volume of data which is exponentially growing [13]. Big data in this study can be obtained from medical screening, pathological data [14] or online medical database.…”

Section: Introductionmentioning

confidence: 99%

Breast cancer diagnosis system using hybrid support vector machine-artificial neural network

Lim¹,

Tay²,

Huong³

et al. 2021

IJECE

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Breast cancer is the second most common cancer occurring in women. Early detection through mammogram screening can save more women’s lives. However, even senior radiologists may over-diagnose the clinical condition. Machine learning (ML) is the most used technique in the diagnosis of cancer to help reduce human errors. This study is aimed to develop a computer-aided detection (CAD) system using ML for classification purposes. In this work, 80 digital mammograms of normal breasts, 40 of benign and 40 of malignant cases were chosen from the mini MIAS dataset. These images were denoised using median filter after they were segmented to obtain a region of interest (ROI) and enhanced using histogram equalization. This work compared the performance of artificial neural network (ANN), support vector machine (SVM), reduced features of SVM and the hybrid SVM-ANN for classification process using the statistical and gray level co-occurrence matrix (GLCM) features extracted from the enhanced images. It is found that the hybrid SVM-ANN gives the best accuracy of 99.4% and 100% in differentiating normal from abnormal, and benign from malignant cases, respectively. This hybrid SVM-ANN model was deployed in developing the CAD system which showed relatively good accuracy of 98%.

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Prediction of Breast Cancer Using Big Data Analytics

Cited by 11 publications

References 1 publication

Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques

Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques

A Comprehensive Big Data Analytics Based Framework for Premature Recognition of Breast Cancer

Breast cancer diagnosis system using hybrid support vector machine-artificial neural network

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