scholarly journals Artificial Bee Colony Optimized Deep Neural Network Model for Handling Imbalanced Stroke Data

2021 ◽  
Vol 12 (5) ◽  
pp. 67-83
Author(s):  
Ajay Dev ◽  
Sanjay Kumar Malik
Keyword(s):  
Neural Network ◽  
Medical Imaging ◽  
Deep Neural Network ◽  
Artificial Bee Colony ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Diagnostic Tools ◽  
Healthcare Data ◽  
Bee Colony ◽  
Learning Techniques

The healthcare domain gets wide attention among the research community due to incremental data growth, advanced diagnostic tools, medical imaging processes, and many more. Enormous healthcare data is generated through diagnostic tool and medical imaging process, but handling of these data is a tough task due to its nature. A large number of machine learning techniques are presented for handling the healthcare data and right diagnosis of disease. However, the accuracy is one of primary concerns regarding the disease diagnosis. Hence, this study explores the applicability of deep neural network (DNN) technique for handling the imbalance of healthcare data. An artificial bee colony technique is adopted to determine the relevant features of stroke disease called ABC-FS-optimized DNN. The performance of proposed ABC-FS-optimized DNN model is evaluated using accuracy, precision, and recall parameters and compared with state of art existing techniques. The simulation results showed that proposed model obtains 87.09%, 84.28%, and 85.72% accuracy, precision, and recall rates, respectively.

scholarly journals Artificial Bee Colony and Deep Neural Network-Based Diagnostic Model for Improving the Prediction Accuracy of Diabetes

2021 ◽  
Vol 12 (2) ◽  
pp. 32-50
Author(s):  
Anand Kumar Srivastava ◽  
Yugal Kumar ◽  
Pradeep Kumar Singh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Artificial Bee Colony ◽  
Diagnostic Tools ◽  
Diagnostic Model ◽  
Learning Approaches ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Effective Diagnosis

A large number of machine learning approaches are implemented in healthcare field for effective diagnosis and prediction of different diseases. The aim of these machine learning approaches is to build automated diagnostic tool for helping the physician as well as monitor the health status of patients. These diagnostic tools are widely adopted in intensive care unit for life expectancy of patients. In this study, an effort is made to design an automated diagnostic model for the diagnosis and prediction of diabetes patients. The proposed diagnostic model is designed using artificial bee colony (ABC) algorithm and deep neural network (DNN) technique, called ABC-DNN-based diagnostic model. The ABC algorithm is applied to determine the relevant features for diabetes prediction and diagnosis while DNN technique is adopted for the prediction and diagnosis of diabetes affected patients. The performance of proposed diagnostic model is tested over Pima Indian Diabetes dataset and evaluated using accuracy, sensitivity, specificity, F-measure, Kappa, and area under curve (AUC) parameters. Further, 10-fold and 50-50% training-testing method are considered to assess the performance of proposed diagnostic model. The experimental results of proposed ABC-DNN model is compared with DNN technique and several existing diabetes studies. It is observed that proposed ABC-DNN model achieves 94.74% accuracy rate using 10-fold method.

scholarly journals Multi-Level Attention Graph Neural Network for Clinically Interpretable Pathway-Level Biomarkers Discovery

2020 ◽  
Author(s):  
Xiaohan Xing ◽  
Fan Yang ◽  
Hang Li ◽  
Jun Zhang ◽  
Yu Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Precision Medicine ◽  
Signaling Pathways ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Proteomic Data ◽  
Diagnosis And Prognosis ◽  
Learning Techniques ◽  
Multi Level ◽  
Pathway Level

Precision medicine, regarded as the future of healthcare, is gaining increasing attention these years. As an essential part of precision medicine, clinical omics have been successfully applied in disease diagnosis and prognosis using machine learning techniques. However, existing methods mainly make predictions based on gene-level individual features or their random combinations, none of the previous work has considered the activation of signaling pathways. Therefore, the model interpretability and accuracy are limited, and reasonable signaling pathways are yet to be discovered. In this paper, we propose a novel multi-level attention graph neural network (MLA-GNN), which applies weighted correlation network analysis (WGCNA) to format the omic data of each patient into graph-structured data, and then constructs multi-level graph features, and fuses them through a well-designed multi-level graph feature fully fusion (MGFFF) module to conduct multi-task prediction. Moreover, a novel full-gradient graph saliency mechanism is developed to make the MLA-GNN interpretable. MLA-GNN achieves state-of-the-art performance on transcriptomic data from TCGA-LGG/TCGA-GBM and proteomic data from COVID-19/non-COVID-19 patient sera. More importantly, the proposed model's decision can be interpreted in the signaling pathway level and is consistent with the clinical understanding.

scholarly journals Applying machine learning in motor activity time series of depressed bipolar and unipolar patients

2020 ◽  
Author(s):  
Petter Jakobsen ◽  
Enrique Garcia-Ceja ◽  
Michael Riegler ◽  
Lena Antonsen Stabell ◽  
Tine Nordgreen ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Motor Activity ◽  
Deep Neural Network ◽  
Machine Learning Techniques ◽  
Healthy Controls ◽  
Activity Time ◽  
Depressed Patients ◽  
Learning Techniques

ABSTRACTCurrent practice of assessing mood episodes in affective disorders largely depends on subjective observations combined with semi-structured clinical rating scales. Motor activity is an objective observation of the inner physiological state expressed in behavior patterns. Alterations of motor activity are essential features of bipolar and unipolar depression. The aim was to investigate if objective measures of motor activity can aid existing diagnostic practice, by applying machine-learning techniques to analyze activity patterns in depressed patients and healthy controls. Random Forrest, Deep Neural Network and Convolutional Neural Network algorithms were used to analyze 14 days of actigraph recorded motor activity from 23 depressed patients and 32 healthy controls. Statistical features analyzed in the dataset were mean activity, standard deviation of mean activity and proportion of zero activity. Various techniques to handle data imbalance were applied, and to ensure generalizability and avoid overfitting a Leave-One-User-Out validation strategy was utilized. All outcomes reports as measures of accuracy for binary tests. A Deep Neural Network combined with random oversampling class balancing technique performed a cut above the rest with a true positive rate of 0.82 (sensitivity) and a true negative rate of 0.84 (specificity). Accuracy was 0.84 and the Matthews Correlation Coefficient 0.65. Misclassifications appear related to data overlapping among the classes, so an appropriate future approach will be to compare mood states intra-individualistic. In summary, machine-learning techniques present promising abilities in discriminating between depressed patients and healthy controls in motor activity time series.

Deep Learning Approach for Extracting Catch Phrases from Legal Documents

2020 ◽  
pp. 143-158
Author(s):  
Kayalvizhi S. ◽  
Thenmozhi D.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Statistical Methods ◽  
Deep Neural Network ◽  
Mean Average Precision ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Legal Documents ◽  
Learning Techniques

Catch phrases are the important phrases that precisely explain the document. They represent the context of the whole document. They can also be used to retrieve relevant prior cases by the judges and lawyers for assuring justice in the domain of law. Currently, catch phrases are extracted using statistical methods, machine learning techniques, and deep learning techniques. The authors propose a sequence to sequence (Seq2Seq) deep neural network to extract catch phrases from legal documents. They have employed several layers, namely embedding layer, encoder-decoder layer, projection layer, and loss layer to build the deep neural network. The methodology is evaluated on IRLeD@FIRE-2017 dataset and the method has obtained 0.787 and 0.607 as mean average precision and recall scores respectively. Results show that the proposed method outperforms the existing systems.

scholarly journals p1-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning

2019 ◽  
Vol 2019 (3) ◽  
pp. 191-209 ◽  
Author(s):  
Se Eun Oh ◽  
Saikrishna Sunkam ◽  
Nicholas Hopper
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Machine Learning Techniques ◽  
Feature Engineering ◽  
Engineering Process ◽  
Learning Techniques ◽  
Wide Range

Abstract Recent advances in Deep Neural Network (DNN) architectures have received a great deal of attention due to their ability to outperform state-of-the-art machine learning techniques across a wide range of application, as well as automating the feature engineering process. In this paper, we broadly study the applicability of deep learning to website fingerprinting. First, we show that unsupervised DNNs can generate lowdimensional informative features that improve the performance of state-of-the-art website fingerprinting attacks. Second, when used as classifiers, we show that they can exceed performance of existing attacks across a range of application scenarios, including fingerprinting Tor website traces, fingerprinting search engine queries over Tor, defeating fingerprinting defenses, and fingerprinting TLS-encrypted websites. Finally, we investigate which site-level features of a website influence its fingerprintability by DNNs.

Abstract 27: Machine Learning Models for Outcome Prediction of Out-Of-Hospital Cardiac Arrest of Presumed Cardiac Cause Using the All-Japan Utstein Registry

Circulation ◽  
2019 ◽  
Vol 140 (Suppl_2) ◽  
Author(s):  
Tomohisa Seki ◽  
Tomoyoshi Tamura ◽  
Kazuhiko Ohe ◽  
Masaru Suzuki
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Logistic Regression ◽  
Cardiac Arrest ◽  
Deep Neural Network ◽  
Outcome Prediction ◽  
Machine Learning Techniques ◽  
Test Set ◽  
Learning Techniques ◽  
Hospital Cardiac Arrest

Background: Outcome prediction for patients with out-of-hospital cardiac arrest (OHCA) using prehospital information has been one of the major challenges in resuscitation medicine. Recently, machine learning techniques have been shown to be highly effective in predicting outcomes using clinical registries. In this study, we aimed to establish a prediction model for outcomes of OHCA of presumed cardiac cause using machine learning techniques. Methods: We analyzed data from the All-Japan Utstein Registry of the Fire and Disaster Management Agency between 2005 and 2016. Of 1,423,338 cases, data of OHCA patients aged ≥18 years with presumed cardiac etiology were retrieved and divided into two groups: training set, n = 584,748 (between 2005 and 2013) and test set, n = 223,314 (between 2014 and 2016). The endpoints were neurologic outcome at 1-month and survival at 1-month. Of 47 variables evaluated during the prehospital course, 19 variables (e.g.,sex, age, ECG waveform, and practice of bystander CPR) were used for outcome prediction. Performances of logistic regression, random forests, and deep neural network were examined in this study. Results: For prediction of neurologic outcomes (cerebral performance category 1 or 2) using the test set, the generated models showed area under the receiver operating characteristic curve (AUROC) values of 0.942 (95% confidence interval [CI] 0.941-0.943), 0.947 (95% CI 0.946-0.948), and 0.948 (95% CI 0.948-0.950) in logistic regression, random forest, and deep neural network, respectively. For survival prediction, the generated models showed AUROC values of 0.901 (95% CI 0.900-0.902), 0.913 (95% CI 0.912-0.914), and 0.912 (95% CI 0.911-0.913) in logistic regression, random forest, and deep neural network, respectively. Conclusions: Machine learning techniques using prehospital variables showed favorable prediction capability for 1-month neurologic outcome and survival in OHCA of presumed cardiac cause.

scholarly journals Galaxy classification: deep learning on the OTELO and COSMOS databases

2020 ◽  
Vol 638 ◽  
pp. A134
Author(s):  
José A. de Diego ◽  
Jakub Nadolny ◽  
Ángel Bongiovanni ◽  
Jordi Cepa ◽  
Mirjana Pović ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Shape Parameter ◽  
Machine Learning Techniques ◽  
Classification Methods ◽  
Neural Network Architecture ◽  
Linear Discriminant ◽  
Learning Techniques ◽  
Galaxy Classification

Context. The accurate classification of hundreds of thousands of galaxies observed in modern deep surveys is imperative if we want to understand the universe and its evolution. Aims. Here, we report the use of machine learning techniques to classify early- and late-type galaxies in the OTELO and COSMOS databases using optical and infrared photometry and available shape parameters: either the Sérsic index or the concentration index. Methods. We used three classification methods for the OTELO database: (1) u − r color separation, (2) linear discriminant analysis using u − r and a shape parameter classification, and (3) a deep neural network using the r magnitude, several colors, and a shape parameter. We analyzed the performance of each method by sample bootstrapping and tested the performance of our neural network architecture using COSMOS data. Results. The accuracy achieved by the deep neural network is greater than that of the other classification methods, and it can also operate with missing data. Our neural network architecture is able to classify both OTELO and COSMOS datasets regardless of small differences in the photometric bands used in each catalog. Conclusions. In this study we show that the use of deep neural networks is a robust method to mine the cataloged data.

scholarly journals An Enhanced Deep Neural Network for Predicting Workplace Absenteeism

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Syed Atif Ali Shah ◽  
Irfan Uddin ◽  
Furqan Aziz ◽  
Shafiq Ahmad ◽  
Mahmoud Ahmad Al-Khasawneh ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Learning Algorithms ◽  
Single Layer ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Proposed Model ◽  
The Cost

Organizations can grow, succeed, and sustain if their employees are committed. The main assets of an organization are those employees who are giving it a required number of hours per month, in other words, those employees who are punctual towards their attendance. Absenteeism from work is a multibillion-dollar problem, and it costs money and decreases revenue. At the time of hiring an employee, organizations do not have an objective mechanism to predict whether an employee will be punctual towards attendance or will be habitually absent. For some organizations, it can be very difficult to deal with those employees who are not punctual, as firing may be either not possible or it may have a huge cost to the organization. In this paper, we propose Neural Networks and Deep Learning algorithms that can predict the behavior of employees towards punctuality at workplace. The efficacy of the proposed method is tested with traditional machine learning techniques, and the results indicate 90.6% performance in Deep Neural Network as compared to 73.3% performance in a single-layer Neural Network and 82% performance in Decision Tree, SVM, and Random Forest. The proposed model will provide a useful mechanism to organizations that are interested to know the behavior of employees at the time of hiring and can reduce the cost of paying to inefficient or habitually absent employees. This paper is a first study of its kind to analyze the patterns of absenteeism in employees using deep learning algorithms and helps the organization to further improve the quality of life of employees and hence reduce absenteeism.

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