Dataset Size Sensitivity Analysis of Machine Learning Classifiers to Differentiate Molecular Markers of Pediatric Low-Grade Gliomas Based on MRI

Abstract Machine learning (ML) approaches can predict BRAF status of pediatric low-grade gliomas (pLGG) on pre-therapeutic brain MRI. The impact of training data sample size and type of ML model is not established. In this bi-institutional retrospective study, 251 pLGG FLAIR MRI datasets from 2 children’s hospitals were included. Radiomics features were extracted from tumor segmentations and five models (Random Forest, XGBoost, Neural Network (NN) 1 (100:20:2), NN2 (50:10:2), NN3 (50:20:10:2)) were tested to classify them. Classifiers were cross-validated on data from institution 1 and validated on data from institution 2. Starting with 10% of the training data, models were cross-validated using a 4-fold approach at every step with an additional 2.25% increase in sample size. Two-hundred-twenty patients (mean age 8.53 ± 4.94 years, 114 males, 67% BRAF fusion) were included in the training dataset, and 31 patients (mean age 7.97±6.20 years, 18 males, 77% BRAF fusion) in the independent test dataset. NN1 (100:20:2) yielded the highest area under the receiver operating characteristic curve (AUC). It predicted BRAF status with a mean AUC of 0.85, 95% CI [0.83, 0.87] using 60% of the training data and with mean AUC of 0.83, 95% CI [0.82, 0.84] on the independent validation data set.

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A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients in China

10.21203/rs.3.rs-152663/v1 ◽

2021 ◽

Author(s):

Dong Wang ◽

JinBo Li ◽

Yali Sun ◽

Xianfei Ding ◽

Xiaojuan Zhang ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Prediction Model ◽

Secondary Analysis ◽

Adverse Outcomes ◽

Training Data ◽

Medical Systems ◽

Validation Data ◽

Data Set ◽

Icu Patients

Abstract Background: Although numerous studies are conducted every year on how to reduce the fatality rate associated with sepsis, it is still a major challenge faced by patients, clinicians, and medical systems worldwide. Early identification and prediction of patients at risk of sepsis and adverse outcomes associated with sepsis are critical. We aimed to develop an artificial intelligence algorithm that can predict sepsis early.Methods: This was a secondary analysis of an observational cohort study from the Intensive Care Unit of the First Affiliated Hospital of Zhengzhou University. A total of 4449 infected patients were randomly assigned to the development and validation data set at a ratio of 4:1. After extracting electronic medical record data, a set of 55 features (variables) was calculated and passed to the random forest algorithm to predict the onset of sepsis.Results: The pre-procedure clinical variables were used to build a prediction model from the training data set using the random forest machine learning method; a 5-fold cross-validation was used to evaluate the prediction accuracy of the model. Finally, we tested the model using the validation data set. The area obtained by the model under the receiver operating characteristic (ROC) curve (AUC) was 0.91, the sensitivity was 87%, and the specificity was 89%.Conclusions: The newly established model can accurately predict the onset of sepsis in ICU patients in clinical settings as early as possible. Prospective studies are necessary to determine the clinical utility of the proposed sepsis prediction model.

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Analysis of non-iterative phase retrieval based on machine learning

Optical Review ◽

10.1007/s10043-019-00574-8 ◽

2020 ◽

Vol 27 (1) ◽

pp. 136-141

Author(s):

Yohei Nishizaki ◽

Ryoichi Horisaki ◽

Katsuhisa Kitaguchi ◽

Mamoru Saito ◽

Jun Tanida

Keyword(s):

Machine Learning ◽

Phase Retrieval ◽

Training Data ◽

Noise Robustness ◽

Data Set ◽

Retrieval Method ◽

Monitoring Applications ◽

Conventional Methods ◽

Noisy Measurements ◽

The Impact

AbstractIn this paper, we analyze a machine-learning-based non-iterative phase retrieval method. Phase retrieval and its applications have been attractive research topics in optics and photonics, for example, in biomedical imaging, astronomical imaging, and so on. Most conventional phase retrieval methods have used iterative processes to recover phase information; however, the calculation speed and convergence with these methods are serious issues in real-time monitoring applications. Machine-learning-based methods are promising for addressing these issues. Here, we numerically compare conventional methods and a machine-learning-based method in which a convolutional neural network is employed. Simulations with several conditions show that the machine-learning-based method realizes fast and robust phase recovery compared with the conventional methods. We also numerically demonstrate machine-learning-based phase retrieval from noisy measurements with a noisy training data set for improving the noise robustness. The machine-learning-based approach used in this study may increase the impact of phase retrieval, which is useful in various fields, where phase retrieval has been used as a fundamental tool.

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A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus

PLoS ONE ◽

10.1371/journal.pone.0242028 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0242028

Author(s):

Hiroaki Haga ◽

Hidenori Sato ◽

Ayumi Koseki ◽

Takafumi Saito ◽

Kazuo Okumoto ◽

...

Keyword(s):

Machine Learning ◽

Hepatitis C Virus ◽

Hepatitis C ◽

Prediction Model ◽

Predictive Model ◽

Machine Learning Algorithms ◽

Training Data ◽

Whole Genome ◽

Validation Data ◽

Data Set

In recent years, the development of diagnostics using artificial intelligence (AI) has been remarkable. AI algorithms can go beyond human reasoning and build diagnostic models from a number of complex combinations. Using next-generation sequencing technology, we identified hepatitis C virus (HCV) variants resistant to directing-acting antivirals (DAA) by whole genome sequencing of full-length HCV genomes, and applied these variants to various machine-learning algorithms to evaluate a preliminary predictive model. HCV genomic RNA was extracted from serum from 173 patients (109 with subsequent sustained virological response [SVR] and 64 without) before DAA treatment. HCV genomes from the 109 SVR and 64 non-SVR patients were randomly divided into a training data set (57 SVR and 29 non-SVR) and a validation-data set (52 SVR and 35 non-SVR). The training data set was subject to nine machine-learning algorithms selected to identify the optimized combination of functional variants in relation to SVR status following DAA therapy. Subsequently, the prediction model was tested by the validation-data set. The most accurate learning method was the support vector machine (SVM) algorithm (validation accuracy, 0.95; kappa statistic, 0.90; F-value, 0.94). The second-most accurate learning algorithm was Multi-layer perceptron. Unfortunately, Decision Tree, and Naive Bayes algorithms could not be fitted with our data set due to low accuracy (< 0.8). Conclusively, with an accuracy rate of 95.4% in the generalization performance evaluation, SVM was identified as the best algorithm. Analytical methods based on genomic analysis and the construction of a predictive model by machine-learning may be applicable to the selection of the optimal treatment for other viral infections and cancer.

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The impact of using large training data set KDD99 on classification accuracy

10.7287/peerj.preprints.2838v1 ◽

2017 ◽

Author(s):

Atilla Özgür ◽

Hamit Erdem

Keyword(s):

Machine Learning ◽

Performance Metrics ◽

Training Data ◽

Supervised Machine Learning ◽

Support Vector ◽

Data Set ◽

Test Dataset ◽

Negative Rate ◽

Positive Rate ◽

The Impact

This study investigates the effects of using a large data set on supervised machine learning classifiers in the domain of Intrusion Detection Systems (IDS). To investigate this effect 12 machine learning algorithms have been applied. These algorithms are: (1) Adaboost, (2) Bayesian Nets, (3) Decision Tables, (4) Decision Trees (J48), (5)Logistic Regression, (6) Multi-Layer Perceptron, (7) Naive Bayes, (8) OneRule, (9)Random Forests, (10) Radial Basis Function Neural Networks, (11) Support Vector Machines (two different training algorithms), and (12) ZeroR. A well-known IDS benchmark dataset, KDD99 has been used to train and test classifiers. Full training data set of KDD99 is 4.9 million instances while full test dataset is 311,000 instances. In contrast to similar previous studies, which used 0.08%–10% for training and 1.2%–100% for testing, this study uses full training dataset and full test dataset. Weka Machine Learning Toolbox has been used for modeling and simulation. The performance of classifiers has been evaluated using standard binary performance metrics: Detection Rate, True Positive Rate, True Negative Rate, False Positive Rate, False Negative Rate, Precision, and F1-Rate. To show effects of dataset size, performance of classifiers has been also evaluated using following hardware metrics: Training Time, Working Memory and Model Size. Test results shows improvements in classifiers in standard performance metrics compared to previous studies.

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The impact of using large training data set KDD99 on classification accuracy

10.7287/peerj.preprints.2838 ◽

2017 ◽

Author(s):

Atilla Özgür ◽

Hamit Erdem

Keyword(s):

Machine Learning ◽

Performance Metrics ◽

Training Data ◽

Supervised Machine Learning ◽

Support Vector ◽

Data Set ◽

Test Dataset ◽

Negative Rate ◽

Positive Rate ◽

The Impact

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Optimization of Support Vector Machine Method Using Feature Selection to Improve Classification Results

JISA(Jurnal Informatika dan Sains) ◽

10.31326/jisa.v4i1.881 ◽

2021 ◽

Vol 4 (1) ◽

pp. 22-27

Author(s):

Saikin Saikin ◽

◽

Sofiansyah Fadli ◽

Maulana Ashari ◽

◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Feature Selection ◽

Training Data ◽

Support Vector ◽

Feature Selection Technique ◽

Validation Data ◽

Machine Method ◽

Testing Data ◽

The Impact

The performance of the organizations or companiesare based on the qualities possessed by their employee. Both of good or bad employee performance will have an impact on productivity and the impact of profits obtained by the company. Support Vector Machine (SVM) is a machine learning method based on statistical learning theory and can solve high non-linearity, regression, etc. In machine learning, the optimization model is a part for improving the accuracy of the model for data learning. Several techniques are used, one of which is feature selection, namely reducing data dimensions so that it can reduce computation in data modeling. This study aims to apply the method of machine learning to the employee data of the Bank Rakyat Indonesia (BRI) company. The method used is SVM method by increasing the accuracy of learning data by using a feature selection technique using a wrapper algorithm. From the results of the classification test, the average accuracy obtained is 72 percent with a precision value of 71 and the recall value is rounded off to 72 percent, with a combination of SVM and cross-validation. Data obtained from Kaggle data, which consists of training data and testing data. each consisting of 30 columns and 22005 rows in the training data and testing data consisting of 29 col-umns and 6000 rows. The results of this study get a classification score of 82 percent. The precision value obtained is rounded off to 82 percent, a recall of 86 percent and an f1-score of 81 percent.

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Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200904164539 ◽

2020 ◽

Vol 13 ◽

Author(s):

Ritu Khandelwal ◽

Hemlata Goyal ◽

Rajveer Singh Shekhawat

Keyword(s):

Machine Learning ◽

Comparative Analysis ◽

Data Science ◽

Training Data ◽

Machine Learning Techniques ◽

Future Trends ◽

Data Set ◽

Learning Stage ◽

Learning Techniques ◽

Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

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Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector

Applied Sciences ◽

10.3390/app9061128 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1128 ◽

Cited By ~ 12

Author(s):

Yundong Li ◽

Wei Hu ◽

Han Dong ◽

Xueyan Zhang

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Hurricane Sandy ◽

Training Data ◽

Aerial Images ◽

Detection Methods ◽

Single Shot ◽

Data Set ◽

Augmentation Strategies ◽

Post Disaster

Using aerial cameras, satellite remote sensing or unmanned aerial vehicles (UAV) equipped with cameras can facilitate search and rescue tasks after disasters. The traditional manual interpretation of huge aerial images is inefficient and could be replaced by machine learning-based methods combined with image processing techniques. Given the development of machine learning, researchers find that convolutional neural networks can effectively extract features from images. Some target detection methods based on deep learning, such as the single-shot multibox detector (SSD) algorithm, can achieve better results than traditional methods. However, the impressive performance of machine learning-based methods results from the numerous labeled samples. Given the complexity of post-disaster scenarios, obtaining many samples in the aftermath of disasters is difficult. To address this issue, a damaged building assessment method using SSD with pretraining and data augmentation is proposed in the current study and highlights the following aspects. (1) Objects can be detected and classified into undamaged buildings, damaged buildings, and ruins. (2) A convolution auto-encoder (CAE) that consists of VGG16 is constructed and trained using unlabeled post-disaster images. As a transfer learning strategy, the weights of the SSD model are initialized using the weights of the CAE counterpart. (3) Data augmentation strategies, such as image mirroring, rotation, Gaussian blur, and Gaussian noise processing, are utilized to augment the training data set. As a case study, aerial images of Hurricane Sandy in 2012 were maximized to validate the proposed method’s effectiveness. Experiments show that the pretraining strategy can improve of 10% in terms of overall accuracy compared with the SSD trained from scratch. These experiments also demonstrate that using data augmentation strategies can improve mAP and mF1 by 72% and 20%, respectively. Finally, the experiment is further verified by another dataset of Hurricane Irma, and it is concluded that the paper method is feasible.

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LGG-18. EVEROLIMUS TREATMENT IN PEDIATRIC PATIENTS AFFECTED BY LOW-GRADE GLIOMAS (pLGG) NON-TSC, BRAF v600-WT

Neuro-Oncology ◽

10.1093/neuonc/noaa222.400 ◽

2020 ◽

Vol 22 (Supplement_3) ◽

pp. iii369-iii369

Author(s):

Antonella Cacchione ◽

Evelina Miele ◽

Maria Chiara Lodi ◽

Andrea Carai ◽

Giovanna Stefania Colafati ◽

...

Keyword(s):

Adverse Event ◽

Disease Progression ◽

Mapk Pathway ◽

Brain Mri ◽

Mammalian Target Of Rapamycin ◽

Low Grade ◽

Duration Of Treatment ◽

Tumor Biopsy ◽

Low Grade Gliomas ◽

Personalized Approach

Abstract BACKGROUND MAPK pathway is the hallmark of pediatric low grade gliomas (pLGGs); hyperactivation of mTOR (mammalian target of rapamycin) might be a suitable biomarker for therapeutic response. We investigated the feasibility of Everolimus, mTOR inhibitor, in patients affected by pLGGs. METHODS Patients 1 to 18 years old, diagnosed with pLGG, with a positive tumor biopsy for mTOR/phospho-mTOR and radiological and / or clinical disease progression, treated at Bambino Gesù Children’s Hospital in Rome were evaluated. Tumor DNA methylation analysis was performed in 10 cases. Exclusion criteria included: Tuberous Sclerosis patients, Sub Ependymal Giant Astrocytoma. Everolimus was administered orally at a dose of 2.5 mg or 5 mg daily based on body weight. Patients were evaluated with brain MRI every 4, 8 and 12 months after treatment start and every six months thereafter. RESULTS 16 patients were enrolled from September 2014 and 2019. The median age was 7.5 years old. All patients had at least one adverse event. Events rated as severe (grade 3/4) were reported in 6 patients. Stomatitis was the most frequent adverse event. One patient discontinued treatment due to grade 4 toxicity (ulcerative stomatitis and fatigue). The median duration of treatment was 21 months (4–57 months). Brain MRI evaluations have showed disease stability in 11 patients, partial response in 2 patients and disease progression in 3 patients. CONCLUSIONS Everolimus has proven to be well tolerated and effective treatment in terms of disease stability in patients with pLGGs. It’s also an excellent example of chemo-free personalized approach.

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Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽

10.1093/genetics/iyab030 ◽

2021 ◽

Author(s):

Marco Lopez-Cruz ◽

Gustavo de los Campos

Keyword(s):

Sample Size ◽

Dna Sequences ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Regularization Parameter ◽

Selection Index ◽

Prediction Method ◽

Training Data ◽

Breeding Value ◽

Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

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