Non-invasive physical demand assessment using wearable respiration sensor and random forest classifier

2021 ◽  
pp. 103279
Author(s):  
Milad Sadat-Mohammadi ◽  
Shahrad Shakerian ◽  
Yizhi Liu ◽  
Somayeh Asadi ◽  
Houtan Jebelli
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Physical Demand ◽  
Non Invasive ◽  
Respiration Sensor ◽  
Demand Assessment

Random Forest Classifier Based ECG Arrhythmia Classification

2012 ◽  
pp. 189-198
Author(s):  
V.Mahesh ◽  
A. Kandaswamy ◽  
C. Vimal ◽  
B. Sathish
Keyword(s):  
Random Forest ◽  
Cardiac Arrhythmias ◽  
Diagnostic System ◽  
Random Forest Classifier ◽  
Practical Applications ◽  
Ecg Signals ◽  
Non Invasive ◽  
Hrv Analysis ◽  
Ecg Arrhythmia Classification ◽  
Ecg Arrhythmia

Heart Rate Variability (HRV) analysis is a non-invasive tool for assessing the autonomic nervous system and for arrhythmia detection and classification. This paper presents a Random Forest classifier based diagnostic system for detecting cardiac arrhythmias using ECG data. The authors use features extracted from ECG signals using HRV analysis and DWT for classification. The experimental results indicate that a prediction accuracy of more than 98% can be obtained using the proposed method. This system can be further improved and fine-tuned for practical applications.

Random Forest Classifier Based ECG Arrhythmia Classification

2010 ◽  
Vol 5 (2) ◽  
pp. 1-10 ◽  
Author(s):  
C. Vimal ◽  
B. Sathish
Keyword(s):  
Random Forest ◽  
Diagnostic System ◽  
Random Forest Classifier ◽  
Arrhythmia Detection ◽  
Practical Applications ◽  
Ecg Signals ◽  
Non Invasive ◽  
Hrv Analysis ◽  
Ecg Arrhythmia Classification ◽  
Ecg Arrhythmia

Heart Rate Variability (HRV) analysis is a non-invasive tool for assessing the autonomic nervous system and for arrhythmia detection and classification. This paper presents a Random Forest classifier based diagnostic system for detecting cardiac arrhythmias using ECG data. The authors use features extracted from ECG signals using HRV analysis and DWT for classification. The experimental results indicate that a prediction accuracy of more than 98% can be obtained using the proposed method. This system can be further improved and fine-tuned for practical applications.

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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