Sensitivity and specificity of machine learning classifiers for ... - SciELO
Recommend Documents
mated perimetry, SAP) measurements as input for machine learning classifiers (MLCs; relevance vector machine, RVM; and subspace mixture of ..... parameters in the solution set),17 we used backward elimination, which we have found to be ...
indices. MoG had significantly greater ROC area than PSD and. CPSD. ...... coma) decision function, we convert the SVM output u(x) into a probabilistic one ...
coherence tomography, OCT) and functional (standard auto- mated perimetry ... Combining tech- ... class membership, e.g., probability of glaucoma defined as ... Jolla, California; and 4VA San Diego Health Services, San Diego, Cali- fornia.
Dec 24, 2014 - remote sensing ... classifiersâsupport vector machine (SVM), normal Bayes (NB), ... decision tree (DT) and support vector machine (SVM).
... of big data, analytics, and cloud computing, the value of algorithms is being proven in ... based systems to detect threats against networks and computer systems. ..... Each vehicle would receive an overall score between 0 and 1, the highest.
and try to clear her mind afterwards (8 seconds of blank screen). ..... more complex classifiers, such as nonlinear SVMs or artificial neural networks, ...... Python version of the toolbox (PyMVPA) available at http://pkg-exppsy.alioth.debian.org.
qualitative information security risk survey is implemented in human resources ... specific assets, vulnerabilities and threats were taken into consideration.
Feb 13, 2017 - the built and natural environment as well as on weather conditions, .... ANNs for travel mode choice prediction, whereas Omrani, Charif, ... deal only with a limited set of machine learning classifiers, even .... For prediction, the cl
Our experiments were conducted using the WEKA machine learning tool on two datasets; the MNIST offline handwritten digits and a collection of online ISGL ...
Prevention team (Siemens CT) which solved this problem is presented in .... engines, medical diagnosis, bioinformatics and cheminformatics, detecting credit card fraud, ...... plausible than other adaptive approaches such as Hidden Markov Models ....
Android malware detection · Anomaly based · Mobile device .... In case of Android malware, system calls, API calls, ..... We made sure that the three-way hand-.
Naïve Bayes outperforms other classifiers in Twitter sentiment analysis and is ... love: products they buy, places they visit, books they are reading, vacations they are ... or in the best cases ill-structured nature of text along with the context .
For spam classification, various spam filtering methods are used. The function of a spam filter is to identify spam email and prevents it from going to the mailbox.
A Study Of Machine Learning Classifiers For Anomaly-Based Mobile Botnet Detection. pp 251-265. 251. Malaysian Journal of Computer Science. Vol. 26(4) ...
to provide competitive Breast Cancer classification schemes reducing the number of employed features for each experimental dataset. I. INTRODUCTION.
localization performance of the algorithms, its variation with the training set size, and ... Moreover, presence monitoring systems can also be used to detect unau-.
Sep 7, 2014 - Methods: This study included 596 elementary school children aged 9 to 12 years (218 females. --- 47.1%). ..... inate between low and high MC (best trade-off between sensitivity and ..... Dallas: The Cooper Institute; 2008. 22.
improved blood pressure assessment and allowed standardizing home monitoring of blood pressure and self-measured blood pressure (SMBP).8 SMBP allows.
ciples outlining their function, both ... OCT tests. The OCT scans were obtained, at. 750 lW, with the 'Fast RNFL Thick- .... SVM were performed in Python. (Python ...
ââ2>IF: 'If there are (at least) two women at the party, Dave will be happy.' (6) a. Jieun-i [gyosu -ne.myeng-i (caki) abstract-lul ilk -ki]. Jieun-NOM professor-4.
1) is a tree of classifiers that is carefully constructed to reduce the average test- time complexity of machine learning algorithms, while maximizing their accuracy.
Mar 13, 2015 - proposed as an etiologic triumvirate that mediates insulin resistance in ... of Maracaibo, the second largest city in Venezuela with. 2.750.00 ...
Cambria E, White B (2014) Jumping NLP curves: a review of natural language ... Poria S, Cambria E, Gelbukh A, Bisio F, Hussain A (2015) Sentiment data flow ...
Sensitivity and specificity of machine learning classifiers for ... - SciELO
(MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT ) and standard ... The best OCT+SAP aROC obtained with RAN (0.946) was significantly.
Artigo Original | Original Article
Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry Sensibilidade e especificidade dos classificadores de aprendizagem de máquina para o diagnóstico de glaucoma usando Spectral Domain OCT e perimetria automatizada acromática Fabrício R. Silva1, Vanessa G. Vidotti1, Fernanda Cremasco1, Marcelo Dias2, Edson S. Gomi2, Vital P. Costa1
ABSTRACT
RESUMO
Purpose: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). Methods: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. Results: Combining OCT and SAP data, MLCs’ aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p
