scholarly journals NIMG-37. PREDICTING SEIZURE IN GLIOMA PATIENTS USING A RANDOM FOREST CLASSIFIER TRAINED ON SEX-SPECIFIC AND MIXED COHORTS

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi169-vi169
Author(s):  
Aditya Khurana ◽  
Sandra Johnston ◽  
Paula Whitmire ◽  
Sara Ranjbar ◽  
Akanksha Sharma ◽  
...  
Keyword(s):  
Random Forest ◽  
Mixed Model ◽  
Cross Validation ◽  
Class Imbalance ◽  
Random Forest Classifier ◽  
Co Morbidity ◽  
Pre Treatment ◽  
Male Model ◽  
Sensitivity Specificity ◽  
Testing Set

Abstract PURPOSE Brain tumor related epilepsy (BTE) is a major co-morbidity in patients with glioma. It is difficult to determine whether the use of anti-epileptic drugs is necessary. We attempted to build a machine-learning model to predict the probability of seizure presentation (SP) with glioma. METHODS We trained a random forest classifier using the following variables: volumetric data of pre-treatment MR images (T1Gd and T2-FLAIR sequences), patient demographics (age; sex), and measurements of tumor proliferation (log(ρ)), invasiveness (log(D)) and their relative ratio (log(ρ/D)). Our cohort consisted of 221 patients total. Using an 80-20 ratio, we used 176 patients (76 SP, 100 nSP) for training and the remaining 45 patients (19 SP, 26 nSP) were used for testing. We also trained on male-only and female-only cohorts to evaluate any sex differences in prediction. For training, 108 males (53 SP, 55 nSP) were used and 28 for testing (14 SP, 14 nSP). We used 72 females (21 SP, 49 nSP) for training and 15 (7 SP, 8 nSP) for testing. We corrected for class imbalance in the female cohort before training. Using 10-fold cross-validation and a separate testing set, we measured performance by ROC curve (AUC), accuracy, sensitivity, and specificity of predictions (average of folds in cross validation). RESULTS The female model achieved the highest AUC (0.853) followed by the mixed model (0.726) and the male model (0.651). In the validation set, the accuracy/sensitivity/specificity of the three cohorts were as follows: mixed (0.726/0.696/0.750), female (0.853/0.830/0.875), and male (0.651/0.577/0.722). The performance of the testing set, in terms of accuracy/sensitivity/specificity were: mixed (0.733/0.74/0.73), female (0.8/0.57/1), and male (0.714/0.64/0.79). CONCLUSION We found a negative correlation between seizure probability and size and invasiveness of tumors. Our model shows promising performance on testing set data. Further cohort studies and training is warranted.

scholarly journals Smart System for Thermal Comfort Prediction on Residential Buildings Using Data-Driven Model with Random Forest Classifier

2021 ◽  
Vol 5 (4) ◽  
pp. 40-45
Author(s):  
O. E. Taylor ◽  
P. S. Ezekiel ◽  
V. T. Emma
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Thermal Comfort ◽  
Residential Buildings ◽  
Random Forest Classifier ◽  
Data Driven ◽  
Smart System ◽  
Building Area ◽  
Using Data ◽  
Testing Set

Building area is a vital consumer of all globally produced energy. Structures of buildings absorb about 40 % of the total energy created which transcription about 30 % of the integral worldwide CO2 radiations. As such, reducing the measure of energy absorbed by the building area would incredibly help the much-crucial depletions in world energy utilization and the related ecological concerns. This paper presents a smart system for thermal comfort prediction on residential buildings using data driven model with Random Forest Classifier. The system starts by acquiring a global thermal comfort data, pre-processed the acquired data, by removing missing values and duplicated values, and also reduced the numbers of features in the dataset by selecting just twelve columns out of 70 columns in total. This process is called feature extraction. After the pre-processing and feature extraction, the dataset was split into a training and testing set. The training set was 70% while the testing set was 30% of the original dataset. The training data was used in training our thermal comfort model with Random Forest Classifier. After training, Random Forest Classifier had an accuracy of 99.99% which is about 100% approximately. We then save our model and deployed to web through python flask, so that users can use it in predicting real time thermal comfort in their various residential buildings.

scholarly journals A learning based framework for diverse biomolecule relationship prediction in molecular association network

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Zhen-Hao Guo ◽  
Zhu-Hong You ◽  
De-Shuang Huang ◽  
Hai-Cheng Yi ◽  
Zhan-Heng Chen ◽  
...  
Keyword(s):  
Random Forest ◽  
Prediction Model ◽  
Computational Models ◽  
Cross Validation ◽  
Random Forest Classifier ◽  
Regulatory Mechanisms ◽  
Association Network ◽  
Great Hope ◽  
Fold Cross Validation ◽  
Graph Factorization

AbstractAbundant life activities are maintained by various biomolecule relationships in human cells. However, many previous computational models only focus on isolated objects, without considering that cell is a complete entity with ample functions. Inspired by holism, we constructed a Molecular Associations Network (MAN) including 9 kinds of relationships among 5 types of biomolecules, and a prediction model called MAN-GF. More specifically, biomolecules can be represented as vectors by the algorithm called biomarker2vec which combines 2 kinds of information involved the attribute learned by k-mer, etc and the behavior learned by Graph Factorization (GF). Then, Random Forest classifier is applied for training, validation and test. MAN-GF obtained a substantial performance with AUC of 0.9647 and AUPR of 0.9521 under 5-fold Cross-validation. The results imply that MAN-GF with an overall perspective can act as ancillary for practice. Besides, it holds great hope to provide a new insight to elucidate the regulatory mechanisms.

The Performance Improvement on 4-FB Face using Random Forest Classifier

2018 ◽  
Vol 10 (5) ◽  
pp. 1-12
Author(s):  
B. Nassih ◽  
A. Amine ◽  
M. Ngadi ◽  
D. Naji ◽  
N. Hmina
Keyword(s):  
Random Forest ◽  
Performance Improvement ◽  
Random Forest Classifier

Methodology for Malware Classification using a Random Forest Classifier

2018 ◽  
Author(s):  
Carlos Domenick Morales-Molina ◽  
Diego Santamaria-Guerrero ◽  
Gabriel Sanchez-Perez ◽  
Hector Perez-Meana ◽  
Aldo Hernandez-Suarez
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Malware Classification

scholarly journals Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elisabeth Sartoretti ◽  
Thomas Sartoretti ◽  
Michael Wyss ◽  
Carolin Reischauer ◽  
Luuk van Smoorenburg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Brain Tumors ◽  
Proton Transfer ◽  
Multilayer Perceptron ◽  
Random Forest Classifier ◽  
Amide Proton ◽  
Low Grade ◽  
Who Grade ◽  
Amide Proton Transfer

AbstractWe sought to evaluate the utility of radiomics for Amide Proton Transfer weighted (APTw) imaging by assessing its value in differentiating brain metastases from high- and low grade glial brain tumors. We retrospectively identified 48 treatment-naïve patients (10 WHO grade 2, 1 WHO grade 3, 10 WHO grade 4 primary glial brain tumors and 27 metastases) with either primary glial brain tumors or metastases who had undergone APTw MR imaging. After image analysis with radiomics feature extraction and post-processing, machine learning algorithms (multilayer perceptron machine learning algorithm; random forest classifier) with stratified tenfold cross validation were trained on features and were used to differentiate the brain neoplasms. The multilayer perceptron achieved an AUC of 0.836 (receiver operating characteristic curve) in differentiating primary glial brain tumors from metastases. The random forest classifier achieved an AUC of 0.868 in differentiating WHO grade 4 from WHO grade 2/3 primary glial brain tumors. For the differentiation of WHO grade 4 tumors from grade 2/3 tumors and metastases an average AUC of 0.797 was achieved. Our results indicate that the use of radiomics for APTw imaging is feasible and the differentiation of primary glial brain tumors from metastases is achievable with a high degree of accuracy.

scholarly journals Fully automated analysis combining [18F]-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression

2021 ◽  
Author(s):  
K. J. Paprottka ◽  
S. Kleiner ◽  
C. Preibisch ◽  
F. Kofler ◽  
F. Schmidt-Graf ◽  
...  
Keyword(s):  
Random Forest ◽  
Tumor Progression ◽  
Automated Analysis ◽  
Random Forest Classifier ◽  
Imaging Modalities ◽  
Amide Proton ◽  
Tumor Board ◽  
Dynamic Susceptibility ◽  
Fet Pet ◽  
Promising Tool

Abstract Purpose To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [18F]-FET-PET and MRI (including amide proton transfer-weighted (APTw) imaging and dynamic-susceptibility-contrast (DSC) perfusion) in differentiation of tumor progression from treatment-related changes in patients with glioma. Material and methods At suspected tumor progression, MRI and [18F]-FET-PET data as part of a retrospective analysis of an observational cohort of 66 patients/74 scans (51 glioblastoma and 23 lower-grade-glioma, 8 patients included at two different time points) were automatically segmented into necrosis, FLAIR-hyperintense, and contrast-enhancing areas using an ensemble of deep learning algorithms. In parallel, previous MR exam was processed in a similar way to subtract preexisting tumor areas and focus on progressive tumor only. Within these progressive areas, intensity statistics were automatically extracted from [18F]-FET-PET, APTw, and DSC-derived cerebral-blood-volume (CBV) maps and used to train a Random Forest classifier with threefold cross-validation. To evaluate contribution of the imaging modalities to the classifier’s performance, impurity-based importance measures were collected. Classifier performance was compared with radiology reports and interdisciplinary tumor board assessments. Results In 57/74 cases (77%), tumor progression was confirmed histopathologically (39 cases) or via follow-up imaging (18 cases), while remaining 17 cases were diagnosed as treatment-related changes. The classification accuracy of the Random Forest classifier was 0.86, 95% CI 0.77–0.93 (sensitivity 0.91, 95% CI 0.81–0.97; specificity 0.71, 95% CI 0.44–0.9), significantly above the no-information rate of 0.77 (p = 0.03), and higher compared to an accuracy of 0.82 for MRI (95% CI 0.72–0.9), 0.81 for [18F]-FET-PET (95% CI 0.7–0.89), and 0.81 for expert consensus (95% CI 0.7–0.89), although these differences were not statistically significant (p > 0.1 for all comparisons, McNemar test). [18F]-FET-PET hot-spot volume was single-most important variable, with relevant contribution from all imaging modalities. Conclusion Automated, joint image analysis of [18F]-FET-PET and advanced MR imaging techniques APTw and DSC perfusion is a promising tool for objective response assessment in gliomas.

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