Breast cancer detection using cartesian genetic programming evolved artificial neural networks

Ahmad, Arbab Masood; Khan, Gul Muhammad; Mahmud, Sahibzada Ali; Miller, Julian F.

doi:10.1145/2330163.2330307

Cited by 51 publications

(17 citation statements)

References 26 publications

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“…e SVM linear on 30 numbers of features achieved 95% accuracy, 96% specificity, 95% sensitivity, 99 F1-score, 5% classification error, and execution time 4.547 Begin (1) Step 1: e preprocessing of breast cancer dataset using preprocessing techniques (2) Step 2: Best Feature selection set by REF algorithm (3) Step 3: Data partition using Training and testing splits method (4) Step 4: Train the predictive model SVM on the Training dataset (5) Step 5: Validation of predictive model SVM using testing dataset (6) Step 6: Computes the model performance evaluation metrics such as accuracy, sensitivity, specificity, MCC, F1-score, and execution time 7Step 7: Finish ALGORITHM 2: Breast cancer predictive system.…”

Section: Classification Results Of Svm (Linear)mentioning

confidence: 99%

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Breast Cancer Detection in the IOT Health Environment Using Modified Recursive Feature Selection

Memon

Haq

et al. 2019

Wireless Communications and Mobile Computing

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The accurate and efficient diagnosis of breast cancer is extremely necessary for recovery and treatment in early stages in IoT healthcare environment. Internet of Things has witnessed the transition in life for the last few years which provides a way to analyze both the real-time data and past data by the emerging role of artificial intelligence and data mining techniques. The current state-of-the-art method does not effectively diagnose the breast cancer in the early stages, and most of the ladies suffered from this dangerous disease. Thus, the early detection of breast cancer significantly poses a great challenge for medical experts and researchers. To solve the problem of early-stage detection of breast cancer, we proposed machine learning-based diagnostic system which effectively classifies the malignant and benign people in the environment of IoT. In the development of our proposed system, a machine learning classifier support vector machine is used to classify the malignant and benign people. To improve the classification performance of the classification system, we used a recursive feature selection algorithm to select more suitable features from the breast cancer dataset. The training/testing splits method is applied for training and testing of the classifier for the best predictive model. Additionally, the classifier performance has been checked on by using performance evaluation metrics such as classification, specificity, sensitivity, Matthews’s correlation coefficient, F1-score, and execution time. To test the proposed method, the dataset “Wisconsin Diagnostic Breast Cancer” has been used in this research study. The experimental results demonstrate that the recursive feature selection algorithm selects the best subset of features, and the classifier SVM achieved optimal classification performance on this best subset of features. The SVM kernel linear achieved high classification accuracy (99%), specificity (99%), and sensitivity (98%), and the Matthews’s correlation coefficient is 99%. From these experimental results, we concluded that the proposed system performance is excellent due to the selection of more appropriate features that are selected by the recursive feature selection algorithm. Furthermore, we suggest this proposed system for effective and efficient early stages diagnosis of breast cancer. Thus, through this system, the recovery and treatment will be more effective for breast cancer. Lastly, the implementation of the proposed system is very reliable in all aspects of IoT healthcare for breast cancer.

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Section: Classification Results Of Svm (Linear)mentioning

confidence: 99%

“…For diagnosis, the breast cancer various invoice-based techniques have been used. In the biopsy technique [3], breast tissues are collected for testing and the results are highly accurate. However, to take a biopsy from the breast is painful for the patient.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Detection in the IOT Health Environment Using Modified Recursive Feature Selection

Memon

Haq

et al. 2019

Wireless Communications and Mobile Computing

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“…Ahmad et al have recently reported good results for CGPANN on the WBC problem, outperforming a number of other published approaches [1]. The system accepts the 30 attributes as described for an exemplar to produce an output value.…”

Section: Case Study: Breast Cancer Detection Using Cgpannmentioning

confidence: 99%

“…From Table 1, it can be observed that approaches employing radial basis functions (RBFs) and the k-neighbor approach have enjoyed increased levels of success on the WBC data set [1] [8] [16]. It is also noted that the CGPANN approach may be favourable to RBF processing elements, as the topology evolving architecture allows for dynamic selection of the number of RBF centres and the consideration of different attributes by each, introducing an element of data mining to the approach.…”

Section: Radial Basis Functionsmentioning

confidence: 99%

“…CGPANN has been demonstrated as a powerful approach in appraisals on the Wisconsin breast cancer (WBC) [22] data set as well as on the single and double pole balancing problems [1,12,13], warranting further evaluation of its use as a tool for bioinformatics. In this paper, we have identified a number of possible enhancements to the CGPANN approach which we will outline and evaluate on the WBC problem.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Performance of CGPANN for Breast Cancer Diagnosis Using Crossover and Radial Basis Functions

Manning

Walsh

2013

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

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Abstract.Recently published evaluations of the topology and weight evolving artificial neural network algorithm Cartesian genetic programming evolved artificial neural networks (CGPANN) have suggested it as a potentially powerful tool for bioinformatics problems. In this paper we provide an overview of the CGPANN algorithm and a brief case study of its application to the Wisconsin breast cancer diagnosis problem. Following from this, we introduce and evaluate the use of RBF kernels and crossover to CGPANN as a means of increasing performance and consistency.

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Supervising Healthcare Schemes Using Machine Learning in Breast Cancer and Internet of Things (SHSMLIoT)

Lamba

Munjal

Gigras

2022

Internet of Healthcare Things

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Breast cancer detection using cartesian genetic programming evolved artificial neural networks

Cited by 51 publications

References 26 publications

Breast Cancer Detection in the IOT Health Environment Using Modified Recursive Feature Selection

Breast Cancer Detection in the IOT Health Environment Using Modified Recursive Feature Selection

Improving the Performance of CGPANN for Breast Cancer Diagnosis Using Crossover and Radial Basis Functions

Supervising Healthcare Schemes Using Machine Learning in Breast Cancer and Internet of Things (SHSMLIoT)

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