Prognosis prediction of human breast cancer by integrating deep neural network and support vector machine: Supervised feature extraction and classification for breast cancer prognosis prediction

AbstractBreast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine.

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An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms

Journal of Sensors ◽

10.1155/2018/1253752 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 12

Author(s):

Abebe Belay Adege ◽

Hsin-Piao Lin ◽

Getaneh Berie Tarekegn ◽

Yirga Yayeh Munaye ◽

Lei Yen

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Deep Neural Network ◽

Support Vector ◽

Feed Forward Neural Network ◽

Online Data ◽

Location Services ◽

Outdoor Environments ◽

Indoor And Outdoor Environments ◽

Indoor And Outdoor

Indoor and outdoor positioning lets to offer universal location services in industry and academia. Wi-Fi and Global Positioning System (GPS) are the promising technologies for indoor and outdoor positioning, respectively. However, Wi-Fi-based positioning is less accurate due to the vigorous changes of environments and shadowing effects. GPS-based positioning is also characterized by much cost, highly susceptible to the physical layouts of equipment, power-hungry, and sensitive to occlusion. In this paper, we propose a hybrid of support vector machine (SVM) and deep neural network (DNN) to develop scalable and accurate positioning in Wi-Fi-based indoor and outdoor environments. In the positioning processes, we primarily construct real datasets from indoor and outdoor Wi-Fi-based environments. Secondly, we apply linear discriminate analysis (LDA) to construct a projected vector that uses to reduce features without affecting information contents. Thirdly, we construct a model for positioning through the integration of SVM and DNN. Fourthly, we use online datasets from unknown locations and check the missed radio signal strength (RSS) values using the feed-forward neural network (FFNN) algorithm to fill the missed values. Fifthly, we project the online data through an LDA-based projected vector. Finally, we test the positioning accuracies and scalabilities of a model created from a hybrid of SVM and DNN. The whole processes are implemented using Python 3.6 programming language in the TensorFlow framework. The proposed method provides accurate and scalable positioning services in different scenarios. The results also show that our proposed approach can provide scalable positioning, and 100% of the estimation accuracies are with errors less than 1 m and 1.9 m for indoor and outdoor positioning, respectively.

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Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering

BioMed Research International ◽

10.1155/2018/2362108 ◽

2018 ◽

Vol 2018 ◽

pp. 1-20 ◽

Cited By ~ 36

Author(s):

Abdullah-Al Nahid ◽

Mohamad Ali Mehrabi ◽

Yinan Kong

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Image Classification ◽

Deep Neural Network ◽

Short Term Memory ◽

Statistical Information ◽

Support Vector ◽

Biomedical Image ◽

Histopathological Images ◽

Photography Analysis

Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identification of cancer largely depends on digital biomedical photography analysis such as histopathological images by doctors and physicians. Analyzing histopathological images is a nontrivial task, and decisions from investigation of these kinds of images always require specialised knowledge. However, Computer Aided Diagnosis (CAD) techniques can help the doctor make more reliable decisions. The state-of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical image analysis. Normally each image contains structural and statistical information. This paper classifies a set of biomedical breast cancer images (BreakHis dataset) using novel DNN techniques guided by structural and statistical information derived from the images. Specifically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification. Softmax and Support Vector Machine (SVM) layers have been used for the decision-making stage after extracting features utilising the proposed novel DNN models. In this experiment the best Accuracy value of 91.00% is achieved on the 200x dataset, the best Precision value 96.00% is achieved on the 40x dataset, and the best F-Measure value is achieved on both the 40x and 100x datasets.

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Perbandingan Teknik Klasifikasi Neural Network, Support Vector Machine, dan Naive Bayes dalam Mendeteksi Kanker Payudara

BINA INSANI ICT JOURNAL ◽

10.51211/biict.v7i1.1343 ◽

2020 ◽

Vol 7 (1) ◽

pp. 53

Author(s):

Derisma Derisma ◽

Fajri Febrian

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Data Mining ◽

Support Vector Machine ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Accuracy Rate ◽

Cancer Disease ◽

Network Support

Abstrak: Kanker payudara merupakan jenis kanker yang sering ditemukan oleh kebanyakan wanita. Di Indonesia Kanker payudara menempati urutan pertama pada pasien rawat inap di seluruh rumah sakit. Tujuan dari penelitian ini adalah melakukan diagnosis penyakit kanker payudara berbasis komputasi yang dapat menghasilkan bagaimana kondisi kanker seseorang berdasarkan akurasi algoritma. Penelitian ini menggunakan pemrograman orange python dan dataset Wisconsin Breast Cancer untuk pemodelan klasifikasi kanker payudara. Metode data mining yang diterapkan yaitu Neural Network, Support Vector Machine, dan Naive Bayes. Dalam penelitian ini didapat algoritma klasifikasi terbaik yaitu algoritma Kernel SVM dengan tingkat akurasi sebesar 98.9 % dan algoritma terendah yaitu Naive Bayes senilai 96.1 %. Kata kunci: kanker payudara, neural network, support vector machine, naive bayes Abstract: Breast cancer is a type of cancer that mostly found in many women. In Indonesia, breast cancer ranks first in hospitalized patients at every hospital. This study aimed to conduct a computation-based diagnose of breast cancer disease that could produce the state of cancer of an individual based on the accuracy of algorithm. This study used python orange programming and Wisconsin Breast Cancer dataset for a modeling and application of breast cancer classification. The data mining methods that were applied in this study were Neural Network, Support Vector Machine, dan Naive Bayes. In this study, Kernel SVM’s algorithm was the best classification algorithm of breast cancer disease with 98.9 % accuracy rate and Naïve Beyes was the lowest with 96.1 % of accuracy rate. Keywords: breast cancer, neural network, support vector machine, naive bayes

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Multi-Stage Feature Selection (MSFS) Algorithm for UWB-Based Early Breast Cancer Size Prediction

10.1101/2020.02.06.936831 ◽

2020 ◽

Author(s):

V. Vijayasarveswari ◽

A.M. Andrew ◽

M. Jusoh ◽

T. Sabapathy ◽

R.A.A. Raof ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Support Vector Machine ◽

Feature Selection ◽

Probabilistic Neural Network ◽

The Other ◽

Ultra Wideband ◽

Support Vector ◽

Data Normalization ◽

Multi Stage

AbstractBreast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multistage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi-stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.

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Evaluating structural safety of trusses using Machine Learning

Frattura ed Integrità Strutturale ◽

10.3221/igf-esis.58.23 ◽

2021 ◽

Vol 15 (58) ◽

pp. 308-318

Author(s):

Tran-Hieu Nguyen ◽

Anh-Tuan Vu

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Roc Curve ◽

Deep Neural Network ◽

Structural Safety ◽

Training Dataset ◽

Support Vector ◽

Machine Model ◽

Adaptive Boosting

In this paper, a machine learning-based framework is developed to quickly evaluate the structural safety of trusses. Three numerical examples of a 10-bar truss, a 25-bar truss, and a 47-bar truss are used to illustrate the proposed framework. Firstly, several truss cases with different cross-sectional areas are generated by employing the Latin Hypercube Sampling method. Stresses inside truss members as well as displacements of nodes are determined through finite element analyses and obtained values are compared with design constraints. According to the constraint verification, the safety state is assigned as safe or unsafe. Members’ sectional areas and the safety state are stored as the inputs and outputs of the training dataset, respectively. Three popular machine learning classifiers including Support Vector Machine, Deep Neural Network, and Adaptive Boosting are used for evaluating the safety of structures. The comparison is conducted based on two metrics: the accuracy and the area under the ROC curve. For the two first examples, three classifiers get more than 90% of accuracy. For the 47-bar truss, the accuracies of the Support Vector Machine model and the Deep Neural Network model are lower than 70% but the Adaptive Boosting model still retains the high accuracy of approximately 98%. In terms of the area under the ROC curve, the comparative results are similar. Overall, the Adaptive Boosting model outperforms the remaining models. In addition, an investigation is carried out to show the influence of the parameters on the performance of the Adaptive Boosting model.

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Heart Rate Variability-Derived Features Based on Deep Neural Network for Distinguishing Different Anaesthesia States

10.21203/rs.3.rs-41792/v2 ◽

2020 ◽

Author(s):

Jian Zhan ◽

Zuo-xi Wu ◽

Zhen-xin Duan ◽

Gui-ying Yang ◽

Zhi-yong Du ◽

...

Keyword(s):

Neural Network ◽

Heart Rate ◽

Support Vector Machine ◽

Logistic Regression ◽

Decision Tree ◽

Deep Neural Network ◽

Low Frequency ◽

Support Vector ◽

Low Frequency Power ◽

Frequency Power

Abstract Background: Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, the hypothesis that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states was investigated.Methods: A novel method of distinguishing different anaesthesia states was developed based on four HRV-derived time and frequency domain features combined with a deep neural network. Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the deep neural network, which used the expert assessment of consciousness level as the reference output. Finally, the deep neural network was compared with the logistic regression, support vector machine, and decision tree models. The datasets of 23 anaesthesia patients were used to assess the proposed method.Results: The accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (logistic regression), 87.5% (support vector machine), 87.2% (decision tree), and 90.1% (deep neural network). The accuracy of deep neural network was higher than those of the logistic regression (p < 0.05), support vector machine (p < 0.05), and decision tree (p < 0.05) approaches. Our method outperformed the logistic regression, support vector machine, and decision tree methods.Conclusions: The incorporation of four HRV-derived time and frequency domain features and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot of a feasibility study, providing a method to supplement DoA monitoring based on EEG features to improve the accuracy of DoA estimation.

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