Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals

Abstract This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable inertial sensors. Measurements were taken from 21 AS patients and 13 non-AS subjects. A feature analysis framework utilizing Elastic Net was implemented to reduce the features generated by continuous wavelet transform (CWT). Performance comparisons were conducted among several machine learning (ML) algorithms, including decision tree, random forest, multi-layer perceptron neural network, and extreme gradient boosting. In addition, a two-dimensional convolutional neural network (2D-CNN) was developed using the CWT coefficients as images. The 2D-CNN was made with a custom-built architecture and a CNN based on Mobile Net via transfer learning. After the reduction of features by 95.47%, the results obtained report 0.87 on accuracy by decision tree, 0.96 by random forest, 0.91 by simple neural network, and 0.95 by XGBoost. Via the 2D-CNN framework, the transfer learning of Mobile Net shows an accuracy of 0.91, while the custom-constructed classifier reveals an accuracy of 0.89. Our results validate the effectiveness of the feature selection and classification framework. They also show a promising potential for the implementation of deep learning tools on the classification of AS.

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Modified Decision Tree Technique for Ransomware Detection at Runtime through API Calls

Scientific Programming ◽

10.1155/2020/8845833 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Faizan Ullah ◽

Qaisar Javaid ◽

Abdu Salam ◽

Masood Ahmad ◽

Nadeem Sarwar ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Decision Tree ◽

Feature Vector ◽

Machine Learning Algorithms ◽

The Novel ◽

Proposed Model ◽

Testing Accuracy ◽

Financial Losses

Ransomware (RW) is a distinctive variety of malware that encrypts the files or locks the user’s system by keeping and taking their files hostage, which leads to huge financial losses to users. In this article, we propose a new model that extracts the novel features from the RW dataset and performs classification of the RW and benign files. The proposed model can detect a large number of RW from various families at runtime and scan the network, registry activities, and file system throughout the execution. API-call series was reutilized to represent the behavior-based features of RW. The technique extracts fourteen-feature vector at runtime and analyzes it by applying online machine learning algorithms to predict the RW. To validate the effectiveness and scalability, we test 78550 recent malign and benign RW and compare with the random forest and AdaBoost, and the testing accuracy is extended at 99.56%.

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Comparative Analysis on Machine Learning and Deep Learning to Predict Post-Induction Hypotension

Sensors ◽

10.3390/s20164575 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4575 ◽

Cited By ~ 1

Author(s):

Jihyun Lee ◽

Jiyoung Woo ◽

Ah Reum Kang ◽

Young-Seob Jeong ◽

Woohyun Jung ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Feature Selection ◽

Deep Learning ◽

Random Forest ◽

Tracheal Intubation ◽

Feature Engineering ◽

Learning Models ◽

Raw Data ◽

Vital Records

Hypotensive events in the initial stage of anesthesia can cause serious complications in the patients after surgery, which could be fatal. In this study, we intended to predict hypotension after tracheal intubation using machine learning and deep learning techniques after intubation one minute in advance. Meta learning models, such as random forest, extreme gradient boosting (Xgboost), and deep learning models, especially the convolutional neural network (CNN) model and the deep neural network (DNN), were trained to predict hypotension occurring between tracheal intubation and incision, using data from four minutes to one minute before tracheal intubation. Vital records and electronic health records (EHR) for 282 of 319 patients who underwent laparoscopic cholecystectomy from October 2018 to July 2019 were collected. Among the 282 patients, 151 developed post-induction hypotension. Our experiments had two scenarios: using raw vital records and feature engineering on vital records. The experiments on raw data showed that CNN had the best accuracy of 72.63%, followed by random forest (70.32%) and Xgboost (64.6%). The experiments on feature engineering showed that random forest combined with feature selection had the best accuracy of 74.89%, while CNN had a lower accuracy of 68.95% than that of the experiment on raw data. Our study is an extension of previous studies to detect hypotension before intubation with a one-minute advance. To improve accuracy, we built a model using state-of-art algorithms. We found that CNN had a good performance, but that random forest had a better performance when combined with feature selection. In addition, we found that the examination period (data period) is also important.

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Deep Learning-Based Classification of Weld Surface Defects

Applied Sciences ◽

10.3390/app9163312 ◽

2019 ◽

Vol 9 (16) ◽

pp. 3312 ◽

Cited By ~ 4

Author(s):

Zhu ◽

Ge ◽

Liu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Surface Defects ◽

Recognition Method ◽

High Level ◽

Intelligent Recognition ◽

Non Destructive

In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical.

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MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions

Frontiers in Oncology ◽

10.3389/fonc.2021.552634 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yanjie Zhao ◽

Rong Chen ◽

Ting Zhang ◽

Chaoyue Chen ◽

Muhetaer Muhelisa ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Decision Tree ◽

Texture Analysis ◽

Training Group ◽

Gradient Boosting ◽

Breast Lesions ◽

Extreme Gradient Boosting ◽

Malignant Breast ◽

Benign Breast Lesions

BackgroundDifferential diagnosis between benign and malignant breast lesions is of crucial importance relating to follow-up treatment. Recent development in texture analysis and machine learning may lead to a new solution to this problem.MethodThis current study enrolled a total number of 265 patients (benign breast lesions:malignant breast lesions = 71:194) diagnosed in our hospital and received magnetic resonance imaging between January 2014 and August 2017. Patients were randomly divided into the training group and validation group (4:1), and two radiologists extracted their texture features from the contrast-enhanced T1-weighted images. We performed five different feature selection methods including Distance correlation, Gradient Boosting Decision Tree (GBDT), least absolute shrinkage and selection operator (LASSO), random forest (RF), eXtreme gradient boosting (Xgboost) and five independent classification models were built based on Linear discriminant analysis (LDA) algorithm.ResultsAll five models showed promising results to discriminate malignant breast lesions from benign breast lesions, and the areas under the curve (AUCs) of receiver operating characteristic (ROC) were all above 0.830 in both training and validation groups. The model with a better discriminating ability was the combination of LDA + gradient boosting decision tree (GBDT). The sensitivity, specificity, AUC, and accuracy in the training group were 0.814, 0.883, 0.922, and 0.868, respectively; LDA + random forest (RF) also suggests promising results with the AUC of 0.906 in the training group.ConclusionThe evidence of this study, while preliminary, suggested that a combination of MRI texture analysis and LDA algorithm could discriminate benign breast lesions from malignant breast lesions. Further multicenter researches in this field would be of great help in the validation of the result.

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Utilização de técnicas de Machine Learning e de Deep Learning para a predição de casos de internações causadas por dengue em municípios da Paraíba

10.5753/ercemapi.2021.17914 ◽

2021 ◽

Author(s):

Ewerthon Dyego de Araújo Batista ◽

Wellington Candeia de Araújo ◽

Romeryto Vieira Lira ◽

Laryssa Izabel de Araújo Batista

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Support Vector Regression ◽

Multilayer Perceptron ◽

Support Vector

Dengue é um problema de saúde pública no Brasil, os casos da doença voltaram a crescer na Paraíba. O boletim epidemiológico da Paraíba, divulgado em agosto de 2021, informa um aumento de 53% de casos em relação ao ano anterior. Técnicas de Machine Learning (ML) e de Deep Learning estão sendo utilizadas como ferramentas para a predição da doença e suporte ao seu combate. Por meio das técnicas Random Forest (RF), Support Vector Regression (SVR), Multilayer Perceptron (MLP), Long ShortTerm Memory (LSTM) e Convolutional Neural Network (CNN), este artigo apresenta um sistema capaz de realizar previsões de internações causadas por dengue para as cidades Bayeux, Cabedelo, João Pessoa e Santa Rita. O sistema conseguiu realizar previsões para Bayeux com taxa de erro 0,5290, já em Cabedelo o erro foi 0,92742, João Pessoa 9,55288 e Santa Rita 0,74551.

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Comparison of machine learning and deep learning techniques in promoter prediction across diverse species

PeerJ Computer Science ◽

10.7717/peerj-cs.365 ◽

2021 ◽

Vol 7 ◽

pp. e365

Author(s):

Nikita Bhandari ◽

Satyajeet Khare ◽

Rahee Walambe ◽

Ketan Kotecha

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Short Term Memory ◽

Regulatory Elements ◽

Gene Promoters ◽

Promoter Prediction ◽

Promoter Sequences ◽

Learning Techniques

Gene promoters are the key DNA regulatory elements positioned around the transcription start sites and are responsible for regulating gene transcription process. Various alignment-based, signal-based and content-based approaches are reported for the prediction of promoters. However, since all promoter sequences do not show explicit features, the prediction performance of these techniques is poor. Therefore, many machine learning and deep learning models have been proposed for promoter prediction. In this work, we studied methods for vector encoding and promoter classification using genome sequences of three distinct higher eukaryotes viz. yeast (Saccharomyces cerevisiae), A. thaliana (plant) and human (Homo sapiens). We compared one-hot vector encoding method with frequency-based tokenization (FBT) for data pre-processing on 1-D Convolutional Neural Network (CNN) model. We found that FBT gives a shorter input dimension reducing the training time without affecting the sensitivity and specificity of classification. We employed the deep learning techniques, mainly CNN and recurrent neural network with Long Short Term Memory (LSTM) and random forest (RF) classifier for promoter classification at k-mer sizes of 2, 4 and 8. We found CNN to be superior in classification of promoters from non-promoter sequences (binary classification) as well as species-specific classification of promoter sequences (multiclass classification). In summary, the contribution of this work lies in the use of synthetic shuffled negative dataset and frequency-based tokenization for pre-processing. This study provides a comprehensive and generic framework for classification tasks in genomic applications and can be extended to various classification problems.

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Modelos de machine learning para predição do sucesso de startups

Revista de Gestão e Projetos ◽

10.5585/gep.v12i2.18942 ◽

2021 ◽

Vol 12 (2) ◽

pp. 28-55

Author(s):

Fabiano Rodrigues ◽

Francisco Aparecido Rodrigues ◽

Thelma Valéria Rocha Rodrigues

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Random Forest ◽

Decision Tree ◽

Initial Public Offering ◽

Gradient Boosting ◽

Support Vector ◽

Trade Offs ◽

Extreme Gradient Boosting ◽

Public Offering

Este estudo analisa resultados obtidos com modelos de machine learning para predição do sucesso de startups. Como proxy de sucesso considera-se a perspectiva do investidor, na qual a aquisição da startup ou realização de IPO (Initial Public Offering) são formas de recuperação do investimento. A revisão da literatura aborda startups e veículos de financiamento, estudos anteriores sobre predição do sucesso de startups via modelos de machine learning, e trade-offs entre técnicas de machine learning. Na parte empírica, foi realizada uma pesquisa quantitativa baseada em dados secundários oriundos da plataforma americana Crunchbase, com startups de 171 países. O design de pesquisa estabeleceu como filtro startups fundadas entre junho/2010 e junho/2015, e uma janela de predição entre junho/2015 e junho/2020 para prever o sucesso das startups. A amostra utilizada, após etapa de pré-processamento dos dados, foi de 18.571 startups. Foram utilizados seis modelos de classificação binária para a predição: Regressão Logística, Decision Tree, Random Forest, Extreme Gradiente Boosting, Support Vector Machine e Rede Neural. Ao final, os modelos Random Forest e Extreme Gradient Boosting apresentaram os melhores desempenhos na tarefa de classificação. Este artigo, envolvendo machine learning e startups, contribui para áreas de pesquisa híbridas ao mesclar os campos da Administração e Ciência de Dados. Além disso, contribui para investidores com uma ferramenta de mapeamento inicial de startups na busca de targets com maior probabilidade de sucesso.

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Different Machine Learning and Deep Learning Methods for the Classification of Colorectal Cancer Lymph Node Metastasis Images

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2020.620257 ◽

2021 ◽

Vol 8 ◽

Author(s):

Jin Li ◽

Peng Wang ◽

Yang Zhou ◽

Hong Liang ◽

Kuan Luan

Keyword(s):

Colorectal Cancer ◽

Machine Learning ◽

Deep Learning ◽

Lymph Node ◽

Lymph Node Metastasis ◽

Transfer Learning ◽

Raw Data ◽

Node Metastasis ◽

Treatment Plans

The classification of colorectal cancer (CRC) lymph node metastasis (LNM) is a vital clinical issue related to recurrence and design of treatment plans. However, it remains unclear which method is effective in automatically classifying CRC LNM. Hence, this study compared the performance of existing classification methods, i.e., machine learning, deep learning, and deep transfer learning, to identify the most effective method. A total of 3,364 samples (1,646 positive and 1,718 negative) from Harbin Medical University Cancer Hospital were collected. All patches were manually segmented by experienced radiologists, and the image size was based on the lesion to be intercepted. Two classes of global features and one class of local features were extracted from the patches. These features were used in eight machine learning algorithms, while the other models used raw data. Experiment results showed that deep transfer learning was the most effective method with an accuracy of 0.7583 and an area under the curve of 0.7941. Furthermore, to improve the interpretability of the results from the deep learning and deep transfer learning models, the classification heat-map features were used, which displayed the region of feature extraction by superposing with raw data. The research findings are expected to promote the use of effective methods in CRC LNM detection and hence facilitate the design of proper treatment plans.

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Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms

Mathematical Problems in Engineering ◽

10.1155/2020/2742781 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 4

Author(s):

Majid Nour ◽

Kemal Polat

Keyword(s):

Machine Learning ◽

Blood Pressure ◽

Random Forest ◽

Decision Tree ◽

Systolic Blood Pressure ◽

Diastolic Blood Pressure ◽

Decision Tree Classifier ◽

Tree Classifier ◽

C4.5 Decision Tree

Hypertension (high blood pressure) is an important disease seen among the public, and early detection of hypertension is significant for early treatment. Hypertension is depicted as systolic blood pressure higher than 140 mmHg or diastolic blood pressure higher than 90 mmHg. In this paper, in order to detect the hypertension types based on the personal information and features, four machine learning (ML) methods including C4.5 decision tree classifier (DTC), random forest, linear discriminant analysis (LDA), and linear support vector machine (LSVM) have been used and then compared with each other. In the literature, we have first carried out the classification of hypertension types using classification algorithms based on personal data. To further explain the variability of the classifier type, four different classifier algorithms were selected for solving this problem. In the hypertension dataset, there are eight features including sex, age, height (cm), weight (kg), systolic blood pressure (mmHg), diastolic blood pressure (mmHg), heart rate (bpm), and BMI (kg/m2) to explain the hypertension status and then there are four classes comprising the normal (healthy), prehypertension, stage-1 hypertension, and stage-2 hypertension. In the classification of the hypertension dataset, the obtained classification accuracies are 99.5%, 99.5%, 96.3%, and 92.7% using the C4.5 decision tree classifier, random forest, LDA, and LSVM. The obtained results have shown that ML methods could be confidently used in the automatic determination of the hypertension types.

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Investigating Transfer Learning for Characterization and Performance Prediction in Unconventional Reservoirs

10.2118/204563-ms ◽

2021 ◽

Author(s):

Jodel Cornelio ◽

Syamil Mohd Razak ◽

Atefeh Jahandideh ◽

Behnam Jafarpour ◽

Young Cho ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Transfer Learning ◽

Method Development ◽

Simulated Data ◽

Production Performance ◽

Unconventional Reservoirs ◽

Eagle Ford ◽

Input Parameters

Abstract Transfer learning is a machine learning concept whereby the knowledge gained (e.g., a model developed) in one task can be transferred (applied) to solve a different but related task. In the context of unconventional reservoirs, the concept can be used to transfer a machine learning model that is learned from data in one field (or shale play) to another, thereby significantly reducing the data needs and efforts to build a new model from scratch. In this work, we study the feasibility of developing deep learning models that can capture and transfer common features in a rich dataset pertaining to a mature unconventional play to enable production prediction in a new unconventional play with limited available data. The focus in this work is on method development using simulated data that correspond to the Bakken and Eagle Ford Shale Plays as two different unconventional plays in the US. We use formation and completion parameter ranges that correspond to the Bakken play with their simulated production responses to explore different approaches for training neural network models that enable transfer learning to predict production responses of input parameters corresponding to the Eagle Ford play (previously unseen input parameters). We explore different schemes by accessing the internal components of the model to extrapolate and categorize salient features that are represented in the trained neural network. Ultimately, our goal is to use these new mechanisms to enable effective sharing and reuse of discovered features from one unconventional well to another. To extract salient trends from formation and completion input parameters and their corresponding simulated production responses, we use deep learning architectures that consist of convolutional encoder-decoder networks. The architecture is then trained with rich simulated data from one field to generate a robust mapping between the input and the output feature spaces. The "learned" parameters from this network can then be "transferred" to develop a different predictive model for another field that may lack sufficient historical data. The results show that using standard training approaches, a neural network model that is trained with sufficiently large data samples from Bakken could produce reliable prediction models for typical wells that may be found in that field. The same neural network, however, could not produce reliable predictions for a typical Eagle Ford well. Furthermore, we observe that a neural network trained with insufficient data samples from Eagle Ford produces a poor prediction model for typical wells that may be found in Eagle Ford. However, when extrapolated feature components of the Bakken neural network were integrated into the training process of the Eagle Ford neural network, the resulting predictions for typical Eagle Ford wells improved significantly. Moreover, we observe that the ability to transfer learning can improve when specialized training strategies are adopted to enable transfer learning. Using several numerical experiments, the paper presents and assesses various transfer learning strategies to predict the production performance of unconventional wells in a new area with limited information by integrating knowledge from more mature plays.

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