Abstract TP458: High Accuracy of Predictive Models for SAH Using Different Machine Learning Approaches

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Paul Litvak ◽  
Jeevan Medikonda ◽  
Girish Menon ◽  
Pitchaiah Mandava
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Prediction Accuracy ◽  
Support Vector ◽  
World Federation ◽  
Learning Approaches ◽  
Flow Models ◽  
Multi Stage ◽  
Stage 1 ◽  
Categorical Scale

Background: Patients suffering from subarachnoid hemorrhage (SAH) have poor long-term outcomes. There are predictive models for ischemic and hemorrhagic stroke. However, there is paucity of models for SAH. Machine learning concepts were applied to build multi-stage Neural Networks (NN), Support Vector Machines (SVM) and Keras/Tensor Flow models to predict SAH outcomes. Methods: A database of ~800 aneurysmal SAH patients from Kasturba Medical College was utilized. Baseline variables of World Federation of Neurosurgeons 5-point scale (WFNS 1-5), age, gender, and presence/absence of hypertension and diabetes were considered in Stage 1. Stage 2 included all Stage 1 variables along with presence/absence of radiologic signs vasospasm and ischemia. Stage 3 includes earlier 2 stages and discharge Glasgow Outcome Scale (GOS 1-5). GOS at 3 months was predicted using 2-layer NN/SVM/Keras-TensorFlow models on the five point categorical scale as well as dichotomized to dead/alive and favorable (GOS 4-5) or unfavorable (GOS 1-3). Prediction accuracy of models was compared to the recorded GOS. Results: Prediction accuracy shown as percentages (See Table) for all three stages was similar for SVM, NN and Keras/TensorFlow models. Accuracy was remarkably higher with dichotomization compared to the complete five point GOS categorical scale. Conclusions: SVM, NN, and Keras-TensorFlow based machine learning models can be used to predict SAH outcomes to a high degree of accuracy. These powerful predictive models can be used to prognosticate and select patients into trials.

scholarly journals Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 263 ◽  
Author(s):  
Meihua Yang ◽  
Dongyun Xu ◽  
Songchao Chen ◽  
Hongyi Li ◽  
Zhou Shi
Keyword(s):  
Machine Learning ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Least Squares ◽  
Paddy Soil ◽  
Prediction Accuracy ◽  
Accurate Determination ◽  
Support Vector ◽  
Learning Approaches ◽  
Lower Yangtze

Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approaches—partial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)—were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R2 = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ significantly between the model with GA (R2 = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R2 = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R2 = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R2 = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R2 = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain.

scholarly journals Ensemble Machine Learning Approaches for Proteogenomic Cancer Studies

2020 ◽  
Author(s):  
Yulan Liang ◽  
Amin Gharipour ◽  
Erik Kelemen ◽  
Arpad Kelemen
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Prediction Accuracy ◽  
Performance Criteria ◽  
Stable Set ◽  
Support Vector ◽  
Kappa Statistics ◽  
Learning Approaches ◽  
Ensemble Machine Learning ◽  
Homogeneous Ensemble

Abstract Background: The identification of important proteins is critical for medical diagnosis and prognosis in common diseases. Diverse sets of computational tools were developed for omics data reductions and protein selections. However, standard statistical models with single feature selection involve the multi-testing burden of low power with the available limited samples. Furthermore, high correlations among proteins with high redundancy and moderate effects often lead to unstable selections and cause reproducibility issues. Ensemble feature selection in machine learning may identify a stable set of disease biomarkers that could improve the prediction performance of subsequent classification models, and thereby simplify their interpretability. In this study, we developed a three-stage homogeneous ensemble feature selection approach for both identifying proteins and improving prediction accuracy. This approach was implemented and applied to ovarian cancer proteogenomics data sets: 1) binary putative homologous recombination deficiency positive or negative; and 2) multiple mRNA classes (differentiated, proliferative, immunoreactive, mesenchymal, and unknown). We conducted and compared various machine learning approaches with homogeneous ensemble feature selection including random forest, support vector machine, and neural network for predicting both binary and multiple class outcomes. Various performance criteria including sensitivity, specificity, kappa statistics were used to assess the prediction consistency and accuracy. Results: With the proposed three-stage homogeneous ensemble feature selection approaches, prediction accuracy can be improved with the limited sample through continuously reducing errors and redundancy, i.e. Treebag provided 83% prediction accuracy (85% sensitivity and 81% specificity) for binary ovarian outcomes. For mRNA multi-classes classification, our approach provided even better accuracy with increased sample size. Conclusions: Despite the different prediction accuracies from various models, homogeneous ensemble feature selection proposed identified consistent sets of top ranked important markers out of 9606 proteins linked to the binary disease and multiple mRNA class outcomes.

scholarly journals Ensemble Machine Learning Approach Improves Predicted Spatial Variation of Surface Soil Organic Carbon Stocks in Data-Limited Northern Circumpolar Region

2020 ◽  
Vol 3 ◽  
Author(s):  
Umakant Mishra ◽  
Sagar Gautam ◽  
William J. Riley ◽  
Forrest M. Hoffman
Keyword(s):  
Machine Learning ◽  
Environmental Factors ◽  
Soil Properties ◽  
Spatial Variation ◽  
Prediction Accuracy ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Approaches ◽  
Regression Kriging ◽  
Soc Stocks

Various approaches of differing mathematical complexities are being applied for spatial prediction of soil properties. Regression kriging is a widely used hybrid approach of spatial variation that combines correlation between soil properties and environmental factors with spatial autocorrelation between soil observations. In this study, we compared four machine learning approaches (gradient boosting machine, multinarrative adaptive regression spline, random forest, and support vector machine) with regression kriging to predict the spatial variation of surface (0–30 cm) soil organic carbon (SOC) stocks at 250-m spatial resolution across the northern circumpolar permafrost region. We combined 2,374 soil profile observations (calibration datasets) with georeferenced datasets of environmental factors (climate, topography, land cover, bedrock geology, and soil types) to predict the spatial variation of surface SOC stocks. We evaluated the prediction accuracy at randomly selected sites (validation datasets) across the study area. We found that different techniques inferred different numbers of environmental factors and their relative importance for prediction of SOC stocks. Regression kriging produced lower prediction errors in comparison to multinarrative adaptive regression spline and support vector machine, and comparable prediction accuracy to gradient boosting machine and random forest. However, the ensemble median prediction of SOC stocks obtained from all four machine learning techniques showed highest prediction accuracy. Although the use of different approaches in spatial prediction of soil properties will depend on the availability of soil and environmental datasets and computational resources, we conclude that the ensemble median prediction obtained from multiple machine learning approaches provides greater spatial details and produces the highest prediction accuracy. Thus an ensemble prediction approach can be a better choice than any single prediction technique for predicting the spatial variation of SOC stocks.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

scholarly journals Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

2021 ◽  
Vol 10 (4) ◽  
pp. 199
Author(s):  
Francisco M. Bellas Aláez ◽  
Jesus M. Torres Palenzuela ◽  
Evangelos Spyrakos ◽  
Luis González Vilas
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Prediction Models ◽  
Support Vector ◽  
False Alarms ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Rías Baixas ◽  
New Algorithms

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system.

Machine Learning Approach for Predicting Lane-Change Maneuvers using the SHRP2 Naturalistic Driving Study Data

2021 ◽  
pp. 036119812110035
Author(s):  
Anik Das ◽  
Mohamed M. Ahmed
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lane Change ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Change Prediction

Accurate lane-change prediction information in real time is essential to safely operate Autonomous Vehicles (AVs) on the roadways, especially at the early stage of AVs deployment, where there will be an interaction between AVs and human-driven vehicles. This study proposed reliable lane-change prediction models considering features from vehicle kinematics, machine vision, driver, and roadway geometric characteristics using the trajectory-level SHRP2 Naturalistic Driving Study and Roadway Information Database. Several machine learning algorithms were trained, validated, tested, and comparatively analyzed including, Classification And Regression Trees (CART), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Naïve Bayes (NB) based on six different sets of features. In each feature set, relevant features were extracted through a wrapper-based algorithm named Boruta. The results showed that the XGBoost model outperformed all other models in relation to its highest overall prediction accuracy (97%) and F1-score (95.5%) considering all features. However, the highest overall prediction accuracy of 97.3% and F1-score of 95.9% were observed in the XGBoost model based on vehicle kinematics features. Moreover, it was found that XGBoost was the only model that achieved a reliable and balanced prediction performance across all six feature sets. Furthermore, a simplified XGBoost model was developed for each feature set considering the practical implementation of the model. The proposed prediction model could help in trajectory planning for AVs and could be used to develop more reliable advanced driver assistance systems (ADAS) in a cooperative connected and automated vehicle environment.

scholarly journals Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1055
Author(s):  
Qian Sun ◽  
William Ampomah ◽  
Junyu You ◽  
Martha Cather ◽  
Robert Balch
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
History Matching ◽  
Optimization Problems ◽  
Learning Technologies ◽  
Petroleum Engineering ◽  
Support Vector ◽  
Learning Approaches ◽  
Field Development ◽  
Proxy Models

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

scholarly journals Detection of Malicious Software by Analyzing Distinct Artifacts Using Machine Learning and Deep Learning Algorithms

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1694
Author(s):  
Mathew Ashik ◽  
A. Jyothish ◽  
S. Anandaram ◽  
P. Vinod ◽  
Francesco Mercaldo ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Support Vector ◽  
Malware Analysis ◽  
Learning Approaches ◽  
Dynamic Features ◽  
System Calls ◽  
Prevention Methods ◽  
Structural Aspects

Malware is one of the most significant threats in today’s computing world since the number of websites distributing malware is increasing at a rapid rate. Malware analysis and prevention methods are increasingly becoming necessary for computer systems connected to the Internet. This software exploits the system’s vulnerabilities to steal valuable information without the user’s knowledge, and stealthily send it to remote servers controlled by attackers. Traditionally, anti-malware products use signatures for detecting known malware. However, the signature-based method does not scale in detecting obfuscated and packed malware. Considering that the cause of a problem is often best understood by studying the structural aspects of a program like the mnemonics, instruction opcode, API Call, etc. In this paper, we investigate the relevance of the features of unpacked malicious and benign executables like mnemonics, instruction opcodes, and API to identify a feature that classifies the executable. Prominent features are extracted using Minimum Redundancy and Maximum Relevance (mRMR) and Analysis of Variance (ANOVA). Experiments were conducted on four datasets using machine learning and deep learning approaches such as Support Vector Machine (SVM), Naïve Bayes, J48, Random Forest (RF), and XGBoost. In addition, we also evaluate the performance of the collection of deep neural networks like Deep Dense network, One-Dimensional Convolutional Neural Network (1D-CNN), and CNN-LSTM in classifying unknown samples, and we observed promising results using APIs and system calls. On combining APIs/system calls with static features, a marginal performance improvement was attained comparing models trained only on dynamic features. Moreover, to improve accuracy, we implemented our solution using distinct deep learning methods and demonstrated a fine-tuned deep neural network that resulted in an F1-score of 99.1% and 98.48% on Dataset-2 and Dataset-3, respectively.

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3068
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-González ◽  
Radim Burget
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
The Other ◽  
Support Vector ◽  
Learning Approaches ◽  
Microscopic Images ◽  
Trained Neural Network ◽  
Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

NIMG-46. SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE ALGORITHMS USING PRE-THERAPY MR IMAGING FOR GLIOMA MOLECULAR SUBTYPE CLASSIFICATION

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi139-vi139
Author(s):  
Jan Lost ◽  
Tej Verma ◽  
Niklas Tillmanns ◽  
W R Brim ◽  
Harry Subramanian ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Prediction Accuracy ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Tumor Resection ◽  
Support Vector ◽  
Hospital Data

Abstract PURPOSE Identifying molecular subtypes in gliomas has prognostic and therapeutic value, traditionally after invasive neurosurgical tumor resection or biopsy. Recent advances using artificial intelligence (AI) show promise in using pre-therapy imaging for predicting molecular subtype. We performed a systematic review of recent literature on AI methods used to predict molecular subtypes of gliomas. METHODS Literature review conforming to PRSIMA guidelines was performed for publications prior to February 2021 using 4 databases: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science core-collection. Keywords included: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Non-machine learning and non-human studies were excluded. Screening was performed using Covidence software. Bias analysis was done using TRIPOD guidelines. RESULTS 11,727 abstracts were retrieved. After applying initial screening exclusion criteria, 1,135 full text reviews were performed, with 82 papers remaining for data extraction. 57% used retrospective single center hospital data, 31.6% used TCIA and BRATS, and 11.4% analyzed multicenter hospital data. An average of 146 patients (range 34-462 patients) were included. Algorithms predicting IDH status comprised 51.8% of studies, MGMT 18.1%, and 1p19q 6.0%. Machine learning methods were used in 71.4%, deep learning in 27.4%, and 1.2% directly compared both methods. The most common algorithm for machine learning were support vector machine (43.3%), and for deep learning convolutional neural network (68.4%). Mean prediction accuracy was 76.6%. CONCLUSION Machine learning is the predominant method for image-based prediction of glioma molecular subtypes. Major limitations include limited datasets (60.2% with under 150 patients) and thus limited generalizability of findings. We recommend using larger annotated datasets for AI network training and testing in order to create more robust AI algorithms, which will provide better prediction accuracy to real world clinical datasets and provide tools that can be translated to clinical practice.

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