Breast Cancer prediction based on Backpropagation Algorithm

Azmi, Muhammad Sufyian Bin Mohd; Cob, Zaihisma Che

doi:10.1109/scored.2010.5703994

Cited by 15 publications

(6 citation statements)

References 14 publications

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“…We have initially take neuron model as 7, 6, 5, 4 and compare our results with paper [2] and we fmd them better as in Table II. We also try to increase the number of neuron models (7,8,9) and calculated correct prediction and miss-classified percentage as in Table III.…”

Section: Resultsmentioning

confidence: 99%

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Breast cancer detection using backpropagation neural network with comparison between different neuron

Pawar¹,

Patil²

2012

2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing

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Breast cancer is an uncontrolled growth of breast cells. Cell in the body get divide, grow and die every day. This division and growth of cell is most of the in orderly manner but when their growth is out of control. The uncontrolled growth of cell forms the lump which is called as tumor. A tumor generally of two types benign (not dangerous) or malignant (dangerous to health). The malignant tumor which develops in breast is called as breast cancer. In this paper we use backpropagation neural network for classification of breast cancer with different neuron models. It can assist doctors for taking correct decisions.

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Section: Resultsmentioning

confidence: 99%

“…with using its activation function to get output signal value (2) and sent that signal to all unit at its above layer (output units).…”

Section: B Training Algorithmmentioning

confidence: 99%

Breast cancer detection using backpropagation neural network with comparison between different neuron

Pawar¹,

Patil²

2012

2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing

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“…Chi et al [32] applied NNs on two different BC datasets to analyze the survival problem and proved that NNs can successfully predict recurrence probability and differentiate patients with good (more than 5 years) and bad (less than 5 years) prognoses. Azmi et al [33] developed a system that can classify BC tumor using feed-forward NN trained with back-propagation algorithm. Paurag et al [34] created a tool using NN for early prediction of BC with the highest accuracy and low error rate.…”

Section: Breast Cancer Management Using Dt and Nnmentioning

confidence: 99%

Breast Cancer Management System Using Decision Tree and Neural Network

2021

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Nowadays, people are facing various health-related problems due to the modern life style what they follow. Breast Cancer is one of the most common problems among women worldwide which affects approximately 2.1 million women each year. Hence, it has become paramount to develop a system that can identify the major risk factors of Breast Cancer beforehand to make women aware about the risk factors and to take some precautionary measures to manage Breast Cancer. Consequently, this paper proposes a system called Transparent Breast Cancer Management System using P-Rules (TBCMS-PR) which identifies the major risk factors responsible for Breast Cancer in detail using decision tree and neural Network. TBCMS-PR uses decision tree to generate the rules for deciding the decision of Breast Cancer. Neural Network is used to keep only the relevant rules for Breast Cancer and to drop the irrelevant ones. Finally, the major risk factors with ranges are identified based on Sequential Search algorithm. The performance of the TBCMS-PR system is validated with the Breast Cancer dataset collected from UCI repository and is compared with a recent existing system. From the experimental results, it is observed that the proposed TBCMS-PR is significant and potential to manage Breast Cancer to a great extent by managing only one or two major risk factor(s).

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“…The training data set that has been used in this paper has been taken from the Wisconsin Breast Cancer Recall and precision share an inversely proportional relationship with each other [18]. This parameter can be calculated using the equation 9 [19]:…”

Section: Datasetmentioning

confidence: 99%

Breast Cancer Prediction using SVM with PCA Feature Selection Method

et al. 2019

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Cancer has been characterized as one of the leading diseases that cause death in humans. Breast cancer, being a subtype of cancer, causes death in one out of every eight women worldwide. The solution to counter this is by conducting early and accurate diagnosis for faster treatment. To achieve such accuracy in a short span of time proves difficult with existing techniques. Also, the medical tests conducted in hospitals for detecting cancer is expensive and is difficult for any common man to afford. To counter these problems, in this paper, we use the concept of applying Support Vector machine a Machine Learning algorithm to predict whether a person is prone to breast cancer. We evaluate the performance of this algorithm by calculating its accuracy and apply a min-max scaling method so as to counter and overcome the problem of overfitting and outliers. After scaling of the dataset, we apply a feature selection method called Principle component analysis to improve the algorithms accuracy by decreasing the number of parameters. The final algorithm has improved accuracy with the absence of overfitting and outliers, thus this algorithm can be used to develop and build systems that can be deployed in clinics, hospitals and medical centers for early and quick diagnosis of breast cancer. The training dataset is from the University of Wisconsin (UCI) Machine Learning Repository which is used to evaluate the performance of the Support vector machine by calculating its accuracy.

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Breast Cancer prediction based on Backpropagation Algorithm

Cited by 15 publications

References 14 publications

Breast cancer detection using backpropagation neural network with comparison between different neuron

Breast cancer detection using backpropagation neural network with comparison between different neuron

Breast Cancer Management System Using Decision Tree and Neural Network

Breast Cancer Prediction using SVM with PCA Feature Selection Method

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