Breast cancer diagnosis using Genetically Optimized Neural Network model

Bhardwaj, Arpit; Tiwari, Aruna

doi:10.1016/j.eswa.2015.01.065

Cited by 209 publications

(95 citation statements)

References 33 publications

(27 reference statements)

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“…In 2015, using genetic programming (GP) based on ANN [77], a new genetically optimized ANN (GOANN) algorithm was proposed to solve classification problems using the WBCD [78]. This paper took the best features of the GP to optimize both the weights and architecture for an ANN, it also proposed modified crossover and mutation operators which expanded the search area and increased the performance.…”

Section: Annsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

198

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Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today's society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.

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Section: Annsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

198

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“…Large researches have been performed in applying genetic algorithms for classification purpose. Arpit Bhardwaj and Aruna Tiwari [10] proposed a model called GONN which hybridize genetic programming with neural network for multi class classification problem. In this algorithm new crossover and mutation operator is defined to reduce the destructive nature of these operators.…”

Section: Amentioning

confidence: 99%

A Survey: Hybrid Intelligent Modeling Technique for Data Classification

Rani¹,

Kumar²

2017

International Journal of Advanced Research in Computer and Comm

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Classification is one of the important areas of research in the field of data mining and machine learning. This paper discusses about GA, ANN and SVM algorithm and their use in classification. The artificial neural network is the widely used technique for classification and prediction. ANN has some disadvantage such long learning rate, high computational cost, convergence at local optima and adjustment of weight. Optimization techniques and hybridization improve ANN performance. GA is an optimization technique that produces optimization of the problem by using natural evolution. SVM use the nonlinear kernel functions that implicitly map input data into high-dimensional feature spaces. Hybridization is a technique which combines the two or more classifier to improve the performance of the classifier.

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“…The results show that the IBPLN model had a higher detection accuracy compared with KNN, Naive Bayes (NB), SVM and Particle swarm optimization models and its value was 93.34%. The Genetically Optimized Neural Network (GONN) [22] model, an ANN models, has been proposed for breast cancer detection on 699 samples. Genetic Programming (GP) is used for testing and training of the GONN model.…”

Section: Related Workmentioning

confidence: 99%

A Modified Model Based on Flower Pollination Algorithm and K-Nearest Neighbor for Diagnosing Diseases

Gashti¹

2018

IIUMEJ

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Exponential growth of medical data and recorded resources from patients with different diseases can be exploited to establish an optimal association between disease symptoms and diagnosis. The main issue in diagnosis is the variability of the features that can be attributed for particular diseases, since some of these features are not essential for the diagnosis and may even lead to a delay in diagnosis. For instance, diabetes, hepatitis, breast cancer, and heart disease, that express multitudes of clinical manifestations as symptoms, are among the diseases with higher morbidity rate. Timely diagnosis of such diseases can play a critical role in decreasing their effect on patients' quality of life and on the costs of their treatment. Thanks to the large data set available, computer aided diagnosis can be an advanced option for early diagnosis of the diseases. In this paper, using a Flower Pollination Algorithm (FPA) and K-Nearest Neighbor (KNN), a new method is suggested for diagnosis. The modified model can diagnose diseases more accurately by reducing the number of features. The main purpose of the modified model is that the Feature Selection (FS) should be done by FPA and data classification should be performed using KNN. The results showed higher efficiency of the modified model on diagnosis of diabetes, hepatitis, breast cancer, and heart diseases compared to the KNN models. ABSTRAK:Pertumbuhan eksponen dalam data perubatan dan sumber direkodkan daripada pesakit dengan penyakit berbeza boleh disalah guna bagi membentuk kebersamaan optimum antara simptom penyakit dan mengenal pasti gejala penyakit (diagnosis). Isu utama dalam diagnosis adalah kepelbagaian ciri yang dimiliki pada penyakit tertentu, sementara ciri-ciri ini tidak penting untuk didiagnosis dan boleh mengarah kepada penangguhan dalam diagnosis. Sebagai contoh, penyakit kencing manis, radang hati, barah payudara dan penyakit jantung, menunjukkan banyak klinikal simptom jelas dan merupakan penyakit tertinggi berlaku dalam masyarakat. Diagnosis tepat pada penyakit tersebut boleh memainkan peranan penting dalam mengurangkan kesan kualiti hidup dan kos rawatan pesakit. Terima kasih kepada set data yang banyak, diagnosis dengan bantuan komputer boleh menjadi pilihan maju menuju ke arah diagnosis awal kepada penyakit. Kertas ini menggunakan Algoritma Flower Pollination (FPA) dan K-Nearest Neighbor (KNN), iaitu kaedah baru dicadangkan bagi diagnosis. Model yang diubah suai boleh mendiagnosis penyakit lebih tepat dengan mengurangkan bilangan ciri-ciri. Tujuan utama model yang diubah suai ini adalah bagi Pemilihan Ciri (FS) perlu dilakukan menggunakan FPA and pengkhususan data perlu dijalankan menggunakan KNN. Keputusan menunjukkan model yang diubah suai lebih cekap dalam mendiagnosis penyakit kencing manis, radang hati, barah payudara dan penyakit jantung berbanding model KNN.

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Breast cancer diagnosis using Genetically Optimized Neural Network model

Cited by 209 publications

References 33 publications

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

A Survey: Hybrid Intelligent Modeling Technique for Data Classification

A Modified Model Based on Flower Pollination Algorithm and K-Nearest Neighbor for Diagnosing Diseases

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