scholarly journals Diagnostic Performance of 2D and 3D T2WI-Based Radiomics Features With Machine Learning Algorithms to Distinguish Solid Solitary Pulmonary Lesion

2021 ◽  
Vol 11 ◽  
Author(s):  
Qi Wan ◽  
Jiaxuan Zhou ◽  
Xiaoying Xia ◽  
Jianfeng Hu ◽  
Peng Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Diagnostic Performance ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Selection Methods ◽  
Linear Discriminant ◽  
2D And 3D

ObjectiveTo evaluate the performance of 2D and 3D radiomics features with different machine learning approaches to classify SPLs based on magnetic resonance(MR) T2 weighted imaging (T2WI).Material and MethodsA total of 132 patients with pathologically confirmed SPLs were examined and randomly divided into training (n = 92) and test datasets (n = 40). A total of 1692 3D and 1231 2D radiomics features per patient were extracted. Both radiomics features and clinical data were evaluated. A total of 1260 classification models, comprising 3 normalization methods, 2 dimension reduction algorithms, 3 feature selection methods, and 10 classifiers with 7 different feature numbers (confined to 3–9), were compared. The ten-fold cross-validation on the training dataset was applied to choose the candidate final model. The area under the receiver operating characteristic curve (AUC), precision-recall plot, and Matthews Correlation Coefficient were used to evaluate the performance of machine learning approaches.ResultsThe 3D features were significantly superior to 2D features, showing much more machine learning combinations with AUC greater than 0.7 in both validation and test groups (129 vs. 11). The feature selection method Analysis of Variance(ANOVA), Recursive Feature Elimination(RFE) and the classifier Logistic Regression(LR), Linear Discriminant Analysis(LDA), Support Vector Machine(SVM), Gaussian Process(GP) had relatively better performance. The best performance of 3D radiomics features in the test dataset (AUC = 0.824, AUC-PR = 0.927, MCC = 0.514) was higher than that of 2D features (AUC = 0.740, AUC-PR = 0.846, MCC = 0.404). The joint 3D and 2D features (AUC=0.813, AUC-PR = 0.926, MCC = 0.563) showed similar results as 3D features. Incorporating clinical features with 3D and 2D radiomics features slightly improved the AUC to 0.836 (AUC-PR = 0.918, MCC = 0.620) and 0.780 (AUC-PR = 0.900, MCC = 0.574), respectively.ConclusionsAfter algorithm optimization, 2D feature-based radiomics models yield favorable results in differentiating malignant and benign SPLs, but 3D features are still preferred because of the availability of more machine learning algorithmic combinations with better performance. Feature selection methods ANOVA and RFE, and classifier LR, LDA, SVM and GP are more likely to demonstrate better diagnostic performance for 3D features in the current study.

scholarly journals Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

2019 ◽  
Vol 20 (9) ◽  
pp. 2185 ◽  
Author(s):  
Xiaoyong Pan ◽  
Lei Chen ◽  
Kai-Yan Feng ◽  
Xiao-Hua Hu ◽  
Yu-Hang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Expression Pattern ◽  
Learning Algorithms ◽  
Expression Patterns ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Feature List ◽  
Cancer Types

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.

scholarly journals Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer

2021 ◽  
Vol 11 (7) ◽  
pp. 3273
Author(s):  
Joana Morgado ◽  
Tania Pereira ◽  
Francisco Silva ◽  
Cláudia Freitas ◽  
Eduardo Negrão ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Feature Selection ◽  
Egfr Mutation ◽  
Feature Selection Method ◽  
Principal Component ◽  
Image Features ◽  
Support Vector ◽  
Selection Methods ◽  
Mutation Status

The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.

scholarly journals Comparing Methods of Feature Extraction of Brain Activities for Octave Illusion Classification Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6407
Author(s):  
Nina Pilyugina ◽  
Akihiko Tsukahara ◽  
Keita Tanaka
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Selection Methods ◽  
Automatic Feature Extraction ◽  
Octave Illusion

The aim of this study was to find an efficient method to determine features that characterize octave illusion data. Specifically, this study compared the efficiency of several automatic feature selection methods for automatic feature extraction of the auditory steady-state responses (ASSR) data in brain activities to distinguish auditory octave illusion and nonillusion groups by the difference in ASSR amplitudes using machine learning. We compared univariate selection, recursive feature elimination, principal component analysis, and feature importance by testifying the results of feature selection methods by using several machine learning algorithms: linear regression, random forest, and support vector machine. The univariate selection with the SVM as the classification method showed the highest accuracy result, 75%, compared to 66.6% without using feature selection. The received results will be used for future work on the explanation of the mechanism behind the octave illusion phenomenon and creating an algorithm for automatic octave illusion classification.

scholarly journals Failure Prediction of Aircraft Equipment Using Machine Learning with a Hybrid Data Preparation Method

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Kadir Celikmih ◽  
Onur Inan ◽  
Harun Uguz
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Performance Criteria ◽  
Aviation Industry ◽  
Support Vector ◽  
Data Preparation ◽  
Hybrid Data

There is a large amount of information and maintenance data in the aviation industry that could be used to obtain meaningful results in forecasting future actions. This study aims to introduce machine learning models based on feature selection and data elimination to predict failures of aircraft systems. Maintenance and failure data for aircraft equipment across a period of two years were collected, and nine input and one output variables were meticulously identified. A hybrid data preparation model is proposed to improve the success of failure count prediction in two stages. In the first stage, ReliefF, a feature selection method for attribute evaluation, is used to find the most effective and ineffective parameters. In the second stage, a K-means algorithm is modified to eliminate noisy or inconsistent data. Performance of the hybrid data preparation model on the maintenance dataset of the equipment is evaluated by Multilayer Perceptron (MLP) as Artificial Neural network (ANN), Support Vector Regression (SVR), and Linear Regression (LR) as machine learning algorithms. Moreover, performance criteria such as the Correlation Coefficient (CC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used to evaluate the models. The results indicate that the hybrid data preparation model is successful in predicting the failure count of the equipment.

scholarly journals Sentiment Analysis Using Hybrid Feature Selection Techniques

2020 ◽  
Vol 4 (1) ◽  
pp. 29
Author(s):  
Sasan Sarbast Abdulkhaliq ◽  
Aso Mohammad Darwesh
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Selection Method ◽  
Machine Learning Algorithms ◽  
Feature Subset Selection ◽  
Support Vector ◽  
Feature Subset

Nowadays, people from every part of the world use social media and social networks to express their feelings toward different topics and aspects. One of the trendiest social media is Twitter, which is a microblogging website that provides a platform for its users to share their views and feelings about products, services, events, etc., in public. Which makes Twitter one of the most valuable sources for collecting and analyzing data by researchers and developers to reveal people sentiment about different topics and services, such as products of commercial companies, services, well-known people such as politicians and athletes, through classifying those sentiments into positive and negative. Classification of people sentiment could be automated through using machine learning algorithms and could be enhanced through using appropriate feature selection methods. We collected most recent tweets about (Amazon, Trump, Chelsea FC, CR7) using Twitter-Application Programming Interface and assigned sentiment score using lexicon rule-based approach, then proposed a machine learning model to improve classification accuracy through using hybrid feature selection method, namely, filter-based feature selection method Chi-square (Chi-2) plus wrapper-based binary coordinate ascent (Chi-2 + BCA) to select optimal subset of features from term frequency-inverse document frequency (TF-IDF) generated features for classification through support vector machine (SVM), and Bag of words generated features for logistic regression (LR) classifiers using different n-gram ranges. After comparing the hybrid (Chi-2+BCA) method with (Chi-2) selected features, and also with the classifiers without feature subset selection, results show that the hybrid feature selection method increases classification accuracy in all cases. The maximum attained accuracy with LR is 86.55% using (1 + 2 + 3-g) range, with SVM is 85.575% using the unigram range, both in the CR7 dataset.

scholarly journals Determining the Geotechnical Slope Failure Factors via Ensemble and Individual Machine Learning Techniques: A Case Study in Mandi, India

2021 ◽  
Vol 9 ◽  
Author(s):  
Naresh Mali ◽  
Varun Dutt ◽  
K. V. Uday
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Slope Failure ◽  
Feature Selection Method ◽  
Failure Prediction ◽  
Selection Method ◽  
Support Vector ◽  
Selection Methods ◽  
Slope Failures ◽  
Causal Factors

Landslide disaster risk reduction necessitates the investigation of different geotechnical causal factors for slope failures. Machine learning (ML) techniques have been proposed to study causal factors across many application areas. However, the development of ensemble ML techniques for identifying the geotechnical causal factors for slope failures and their subsequent prediction has lacked in literature. The primary goal of this research is to develop and evaluate novel feature selection methods for identifying causal factors for slope failures and assess the potential of ensemble and individual ML techniques for slope failure prediction. Twenty-one geotechnical causal factors were obtained from 60 sites (both landslide and non-landslide) spread across a landslide-prone area in Mandi, India. Relevant causal factors were evaluated by developing a novel ensemble feature selection method that involved an average of different individual feature selection methods like correlation, information-gain, gain-ratio, OneR, and F-ratio. Furthermore, different ensemble ML techniques (Random Forest (RF), AdaBoost (AB), Bagging, Stacking, and Voting) and individual ML techniques (Bayesian network (BN), decision tree (DT), multilayer perceptron (MLP), and support vector machine (SVM)) were calibrated to 70% of the locations and tested on 30% of the sites. The ensemble feature selection method yielded six major contributing parameters to slope failures: relative compaction, porosity, saturated permeability, slope angle, angle of the internal friction, and in-situ moisture content. Furthermore, the ensemble RF and AB techniques performed the best compared to other ensemble and individual ML techniques on test data. The present study discusses the implications of different causal factors for slope failure prediction.

A Topical Survey: Applications of Machine Learning in Medical Issues

2020 ◽  
Vol 17 (11) ◽  
pp. 5010-5019
Author(s):  
Chapala Maharana ◽  
Bijan Bihari Mishra ◽  
Ch. Sanjeev Kumar Dash
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Image Segmentation ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Lesion Detection ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Medical Issues

Computational Intelligence methods have replaced almost all real world applications with high accuracy within the given time period. Machine Learning approaches like classification, feature selection, feature extraction have solved many problems of different domain. They use different ML models implemented with suitable ML tool or combination of tools from NN (Neural Network), SVM (Support Vector Machine), DL (Deep Learning), ELM (Extreme Learning Machine). The model is used for training with known data along with ML algorithms (fuzzy logic, genetic algorithm) to optimize the accuracy for different medical issues for example gene expression and image segmentation for information extraction and disease diagnosis, health monitoring, disease treatment. Most of the medical problems are solved using recent advances in AI (Artificial Intelligence) technologies with the biomedical systems development (e.g., Knowledge based Decision Support Systems) and AI technologies with medical informatics science. AI based methods like machine learning algorithms implemented models are increasingly found in real life applications ex. healthcare, natural calamity detection and forecasting. There are the expert systems handled by experts for knowledge gain which is used in decision making applications. The ML models are found in different medical applications like disease diagnosis (ex. cancer prediction, diabetics disease prediction) and for treatment of diseases (ex. in diabetics disease the reduction in mean glucose concentration following intermittent gastric feeds). The feature selection ML method is used for EEG classification for detection of the severity of the disease in heart related diseases and for identification of genes in different disorder like autism disorder. The ML models are found in health record systems. There are other applications of ML approaches found in image segmentation, tissue extraction, image fragmentation for disease diagnosis (ex. lesion detection in breast cancer for malignancy) and then treatment of those diseases. ML models are found in mobile health treatment, treatment of psychology patients, treatment of dumb patients etc. Medical data handling is the vital part of health care systems for the development of AI systems which can again be solved by machine learning approaches. The ML approaches for medical issues have used ensemble methods or combinations of machine learning tools and machine learning algorithms to optimize the result with good accuracy value at a faster rate.

scholarly journals Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-26
Author(s):  
Abdurrahman Burak Guher ◽  
Sakir Tasdemir ◽  
Bulent Yaniktepe
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Solar Radiation ◽  
Target Location ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Input Variables ◽  
Target Locations ◽  
Estimation Models

The precise estimation of solar radiation is of great importance in solar energy applications with respect to installation and capacity. In estimate modelling on selected target locations, various computer-based and experimental methods and techniques are employed. In the present study, the Multilayer Feed-Forward Neural Network (MFFNN), K -Nearest Neighbors ( K -NN), a Library for Support Vector Machines (LibSVM), and M5 rules algorithms, which are among the Machine Learning (ML) algorithms, were used to estimate the hourly average solar radiation of two geographic locations on the same latitude. The input variables that had the most impact on solar radiation were identified and grouped as a result of 29 different applications that were developed by using 6 different feature selection methods with Waikato Environment for Knowledge Analysis (WEKA) software. Estimation models were developed by using the selected data groups and all input variables for each target location. The results show that the estimations developed with the feature selection method were more successful for target locations, and the radiation potentials were similar. The performance of the estimation models was evaluated by comparing each model with different statistical indicators and with previous studies. According to the RMSE, MAE, R 2 , and SMAPE statistical scales, the results of the most successful estimation models that were developed with MFFNN were 0.0508-0.0536, 0.0341-0.0352, 0.9488-0.9656, and 7.77%-7.79%, respectively.

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.

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