Coarse-Grained Sentiment Analysis Berbasis Natural Language Processing – Ulasan Hotel

295: Accuracy of Various Methods of B-Line Assessment In the Diagnosis of Congestive Heart Failure Compared by Receiver Operating Characteristic Curves

Receiver Operating Characteristic (ROC) curves can be used to describe detectability of noise-limited signals. The basic concepts underlying the theory of ROC curves and the experimental determination of ROC curves are discussed. ROC curves are used to describe the detectability of the image of 2 mm Lucite beads (similar to certain kinds of small gall-stones) in a noisy background of radiographic mottle. Results are shown for four typical radiographic screen-film combinations of differing optical and noise properties. The qualitative relationship between the ROC curves and the mathematical model describing radiographic mottle is examined. The ROC curves describing the detectability of the bad images when radiographed with two screen-film combinations of equal system speed but different optical characteristics are compared to the quality of blood vessel images obtained with the same screen-film combinations. It is concluded that it is important to distinguish between the assessment of image quality based on the relative sharpness of high contrast images, versus the assessment of image quality based on the relative detectability of low contrast noise-limited images.

Objective To investigate features of fasting plasma glucose (FPG) during different trimesters and hemoglobin A1c (HbA1c) in the third trimester of pregnant women in Zhujing Region of Shanghai. Methods A total of 606 cases of pregnant women who received prenatal examination and 75 g oral glucose tolerance test (OGTT) in Jinshan Branch of Shanghai Sixth People′s Hospital from January 1 to December 31, 2014 were collected as research subjects. According to the results of 75 g OGTT, they were divided into gestational diabetes mellitus (GDM) group (n=136) and non-GDM group (n=470). The clinical data, such as age, body mass index (BMI), as well as the concentration of FPG during different pregnancy trimesters and level of HbA1c in the third trimester were collected retrospectively in both two groups by retrospective analysis method. According to the quartile method, level of HbA1c in the third trimester of all subjects were divided into four ranges: Q1 (HbA1c level<5.1%), Q2 (5.1%≤HbA1c level<5.2%), Q3 (5.2%≤HbA1c level<5.5%), and Q4 (HbA1c level≥5.5%), respectively. The age, BMI, concentrations of FPG and the level of HbA1c between two groups were compared by independent-samples t test. Chi-square test was used to compare the GDM incidence of all subjects with different ranges of HbA1c level in the third trimester, and further comparison was conducted by adjusting inspection level. Then receiver operator characteristic (ROC) curve of HbA1c level in the third trimester for predicting the incidence of GDM was drawn, and the area under ROC curve (ROC-AUC) was calculated. The optimal critical value of HbA1c level in the third trimester for predicting the incidence of GDM was obtained when the Youden index reaching the maximum value. And its sensitivity and specificity were calculated. This study met the requirements of the World Medical Association Declaration of Helsinki revised in 2013. Results ①The age and BMI of GDM group were higher than those in non-GDM group, and there were statistically significant differences (t=5.306, 5.250; P<0.001). Among the subjects in GDM group, the concentration of FPG in second trimester was the highest among 3 trimesters, which was (4.9±0.6) mmol/L, and the concentration of FPG in second trimester was higher than that in first and third trimester, respectively, and both the differences were statistically significant (t=2.087, 1.960; P=0.039, 0.041). Among the subjects in non-GDM group, the concentration of FPG in first trimester was the highest among 3 trimesters, which was (4.6±0.3) mmol/L, and the concentration of FPG in first trimester was higher than that in second and third trimester, respectively, and both the differences were statistically significant (t=15.230, 5.613; P<0.001). The concentration of FPG in first, second and third trimesters of GDM group were higher than those of non-GDM group, respectively, and there were statistically significant differences (t=5.416, 15.526, 4.471; P<0.001). Besides, HbA1c level in third trimester of GDM group was (5.6±0.4)%, which was higher than that of non-GDM group (5.4±0.4)%, and there was significant difference (t=5.845, P<0.01). ②There was statistical difference in GDM incidences in all subjects with different ranges of HbA1c levels in third trimester (χ2=22.707, P<0.001). Multiple comparison results showed that the incidences of GDM in all subjects with Q1 and Q2 ranges of HbA1c levels in third trimester were higher than that in subjects with Q4 range of HbA1c levels in third trimester, respectively, and both the differences were statistically significant (χ2=15.071, 16.785; P<0.001). ③The results of ROC curve analysis of HbA1c level in third trimester in predicting the incidence of GDM showed that the ROC-AUC was 0.647 (95%CI: 0.625-0.672, P<0.001), and the optimal cut-off value of HbA1c level in third trimester to predict GDM incidence was 5.5%, and the sensitivity of HbA1c level in predicting the incidence of GDM was 63.4%, and the sensitivity was 69.7%. Conclusions The concentrations of FPG in GDM pregnant women in Zhujing Region of Shanghai begin to increase from the first trimester and peak during the second trimester, and decrease during the third trimester after lifestyle intervention. It is advisable for GDM pregnant women to control HbA1c levels below 5.5% during the third trimester. Key words: Diabetes, gestational; Fasting plasma glucose; Hemoglobin A, glycosylated; Glucose tolerance test; ROC curve; Forecasting; Pregnant women

A receiver operating characteristic (ROC) curve visually demonstrates the tradeoff between sensitivity and specificity as a function of varying a classification threshold. It is a common practice to use ROC curves to measure the accuracy of predictions by different methods. Although this method has been used primarily in medical and engineering fields, it could be used effectively in sports as well. More precisely, an ROC plots the sensitivity versus (1 - specificity), and the area under the curve gives a measure of the prediction. So, the ideal best prediction should have one square unit of area under the ROC curve, where it achieves both 100% sensitivity and 100% specificity (which, in reality, rarely happens). Consequently, when we compare two methods, the one with the greater area under its ROC is judged best. This paper shows the effectiveness of using the ROC curves in analyzing cricket data. In particular, the quality of the decisions made by umpires is investigated. Also, the comparison of the accuracy of the methods for revising the target for matches shortening due to weather interruptions is the key interest in our investigation.

We consider the distributed detection problem, in which a set of decision makers (DMs) receive observations of the environment and transmit finite-valued messages to other DMs according to prespecified communication protocols. A designated primary DM makes the final decision on one out of two alternative hypotheses. All DMs make decisions, in order to maximize a measure of organizational performance. We discuss three different types of decision rules (deterministic, independent randomization, and dependent randomization), and their implications on the organizational performance. Each DM is described by its individual Receiver Operating Characteristic (ROC) curve, which is concave. We determine that concavity is not guaranteed for the case of the ROC curve of a team of DMs, even if the decision rules are perfectly continuous and if the individual ROC curves are strictly concave and smooth, unless dependent randomization is allowed.

In power distribution fault data, the percentage of faults with different causes could be very different and varies from region to region. This data imbalance issue seriously affects the performance evaluation of fault diagnosis algorithms. Due to the limitations of conventional accuracy (ACC) and geometric mean (G-mean) measures, this paper discusses the application of Receiver Operating Characteristic (ROC) curves in evaluating distribution fault diagnosis performance. After introducing how to obtain ROC curves, Artificial Neural Networks (ANN), Logistic Regression (LR), Support Vector Machines (SVM), Artificial Immune Recognition Systems (AIRS), and K-Nearest Neighbor (KNN) algorithm are compared using ROC curves and Area Under the Curve (AUC) on real-world fault datasets from Progress Energy Carolinas. Experimental results show that AIRS performs best most of the time and ANN is potentially a good algorithm with a proper decision threshold.

In this paper we propose a combination of the AdaBoost and random forests algorithms for constructing a breast cancer survivability prediction model. We use random forests as a weak learner of AdaBoost for selecting the high weight instances during the boosting process to improve accuracy, stability and to reduce overfitting problems. The capability of this hybrid method is evaluated using basic performance measurements (e.g., accuracy, sensitivity, and specificity), Receiver Operating Characteristic (ROC) curve and Area Under the receiver operating characteristic Curve (AUC). Experimental results indicate that the proposed method outperforms a single classifier and other combined classifiers for the breast cancer survivability prediction.

To assess examiner reliability when scoring dental fluorosis in Malaysian children using clinical (Dean's Index) and photographic methods. The upper central incisors of 111 children were examined both clinically and photographically for fluorosis status using Dean's index. Twenty children were re-examined after a two-week interval for intra-examiner reliability by a single examiner. In addition, two independent examiners and the clinical examiner scored 111 photographic images of the same children in a standardized manner. Fluorosis scores were compared individually between examiners for both clinical and photographic scoring. Examiner reliability was assessed using both simple and weighted kappa statistics at tooth level. Sensitivity, specificity, positive-negative predictive values and a Receiver Operating Characteristic (ROC) curve were also calculated to determine the accuracy of the test. Across the three examiners, the prevalence of fluorosis (Dean's score ≥ 2) using photographs was lower (ranged from 23% to 26%) than the prevalence recorded by clinical examination (30%). The kappa score for intra-examiner reliability for the duplicate clinical examination was excellent (0.89). Inter-examiner reliability between the photographic method and the clinical examination (gold standard) for each examiner was substantial with weighted kappa values ranging from 0.74 to 0.77. The photographic method indicated higher specificity (99%) than sensitivity (79%) and the area under the ROC curve was also high (0.89) which suggests good accuracy of the diagnostic test. These results suggest that photographic examination of fluorosis on central incisors can be recorded with good examiner reliability. The recorded fluorosis prevalence was lower using the photographic scores.

Research Type: Level 3 - Retrospective cohort study, Case-control study, Meta-analysis of Level 3 studies Introduction/Purpose: The recently proposed Progressive Collapsing Foot Deformity (PCFD) classification system categorizes different deformity patterns into five classes (A, Hindfoot Valgus; B, Midfoot/Forefoot Abduction; C, Arch Collapse; D, Peritalar Subluxation; E, Ankle Valgus Instability). Currently, the decision-making process to determine the presence or absence of each deformity class relies on the surgeon's experience and interpretation of clinical and radiographic assessments, without established cut-off threshold values in the literature for PCFD measurements. This limits the reliability and reproducibility of the classification system. The goal of the current study was to establish cut-off threshold values for the most commonly used PCFD measurements assessing Class A to D deformities, using a large cohort of PCFD patients and controls. Methods: Two patient cohorts were included: a prospective cohort consisting of normally aligned and asymptomatic control volunteers (n=197 patients, 103 females/94 males, average age 41.7 years, average BMI 28.9) and a retrospective cohort of PCFD patients (n=321 patients, 136 females/185 males, average age 50.7 years, average BMI 29.8). All patients underwent Weight-Bearing Computed Tomography (WBCT) scans of the foot and ankle. Foot bone segmentation was performed semi-automatically using specialized software, followed by automatic measurements of various parameters including: Class A (Hindoot Moment Arm – HMA), Class B (Talonavicular Coverage Angle – TCA; Talus-First Metatarsal Angle Axial – TFMA-A), Class C (Talus-First Metatarsal Angle Sagittal – TFMA-S; Forefoot Arch Angle - FAA), Class D (Coverage Maps of the Subtalar Joint Middle-MF, Posterior Facets-PF; and Sinus Tarsi-ST). Receiver Operating Characteristic (ROC) curves, Youden's Indexes and Areas Under the Curve (AUC) were calculated for each measurement. P-values of 0.05 or less were considered significant. Results: The Cut-Off Threshold Values identified by the ROC curves using Youden’s Index for different PCFD measurements were as follows (all p-values &lt; 0.0001): HMA of 13.9mm or more (AUC 85.3%) was considered diagnostic for Class A PCFD deformity. For Class B PCFD deformity, a TNCA of 38.7o or higher (AUC 84.4%) and TFMA-A of 20.3o or higher (AUC 82.1%) were considered diagnostic. For Class C PCFD deformity, a FAA of 8.7o or lower (AUC 83.9%) and TFMA-S of 18.7o or higher (AUC 82.9%) were diagnostic. For Class D Deformity, Coverage values for Middle Facet of 73.5% or lower (AUC 73.4%), Posterior Facet of 84.5% or lower (AUC 82.7%), and Sinus Tarsi of 25.7% or higher (AUC 84.4%) were diagnostic. Conclusion: In this diagnostic study involving 312 PCFD patients and 197 volunteer controls, we successfully determined the cut-off threshold values for key PCFD measurements related to Hindfoot Valgus (Class A), Midfoot/Forefoot Abduction (Class B), Arch Collapse (Class C), and Peritalar Subluxation (Class D) deformities. By utilizing a large cohort of patients and controls, we achieved high diagnostic accuracy for most measurements assessed (AUC of 80% or more) in identifying the presence or absence of these PCFD deformity classes. Our findings can potentially serve as normative threshold values to enhance the utilization, reliability, and reproducibility of the PCFD classification system. Figure 1 Scatter plots and ROC (Receiver Operating Characteristic) curves used to analyze parameters for the various deformity classifications in Progressive Collapsing Foot Deformity (PCFD). For the scatter plots, red indicates a normal control, green indicates an asymptomatic PCFD, and blue indicates a symptomatic PCFD.

BackgroundCatastrophic antiphospholipid syndrome (CAPS) is a life-threatening form of antiphospholipid syndrome (APS), with rarely occurrence but high mortality. Current classification diagnosis criteria require more than 3 organs involvement in 1-week...

In this study, the relationship between natural gas (NG) data and gas-related environmental elements was performed using machine learning algorithms to predict the level of gas leakage risk without directly measuring gas leakage data. The study was based on open data provided by the server using the IoT-based remote control Picarro gas sensor specification. The naturel gas leaks into the air, it is a big problem for air pollution, environment and the health. The proposed method is multivariate outlier removing method based Random Forest (RF) classification for predicting risk of NG leak. After, unsupervised k-means clustering, the experimental dataset has done imbalanced data. Therefore, we focusing our proposed models can predict medium and high risk so best. In this case, we compared the receiver operating characteristic (ROC) curve, accuracy, area under the ROC curve (AUC), and mean standard error (MSE) for each classification model. As a result of our experiments, the evaluation measurements include accuracy, area under the ROC curve (AUC), and MSE; 99.71%, 99.57%, and 0.0016 for MOL_RF respectively.

From a fresh data science perspective, this thesis discusses the prediction of coronary artery disease based on Single-Nucleotide Polymorphisms (SNPs) from the Ontario Heart Genomics Study (OHGS). First, the thesis explains the k-Nearest Neighbour (k-NN) and Random Forest learning algorithms, and includes a complete proof that k-NN is universally consistent in finite dimensional normed vector spaces. Second, the thesis introduces two dimensionality reduction techniques: Random Projections and a new method termed Mass Transportation Distance (MTD) Feature Selection. Then, this thesis compares the performance of Random Projections with k-NN against MTD Feature Selection and Random Forest for predicting artery disease. Results demonstrate that MTD Feature Selection with Random Forest is superior to Random Projections and k-NN. Random Forest is able to obtain an accuracy of 0.6660 and an area under the ROC curve of 0.8562 on the OHGS dataset, when 3335 SNPs are selected by MTD Feature Selection for classification. This area is considerably better than the previous high score of 0.608 obtained by Davies et al. in 2010 on the same dataset.

Receiver operating characteristic (ROC) analysis has been used in a variety of settings since it was first declassified by the United States government over 60 years ago. One venue in which it has received particular attention is in the field of radiology. In radiology, as in other areas of application, ROC analysis is used to assess the ability of a diagnostic test to distinguish between two opposing states. One useful descriptor in ROC analysis is the area under the ROC curve. At times, it is useful and insightful to average ROC curves in order to create a single curve that summarizes all of the data from multiple readers. In this paper, we investigate the impact of correlated readings on the average area under two readers' ROC curves using several common averaging strategies, and then apply the results to a radiologic study.

Over the last few decades, deforestation and climate change have caused increasing number of forest fires. In Southeast Asia, Indonesia has been the most affected country by tropical peatland forest fires. These fires have a significant impact on the climate resulting in extensive health, social and economic issues. Existing forest fire prediction systems, such as the Canadian Forest Fire Danger Rating System, are based on handcrafted features and require installation and maintenance of expensive instruments on the ground, which can be a challenge for developing countries such as Indonesia. We propose a novel, cost-effective, machine-learning based approach that uses remote sensing data to predict forest fires in Indonesia. Our prediction model achieves more than 0.81 area under the receiver operator characteristic (ROC) curve, performing significantly better than the baseline approach which never exceeds 0.70 area under ROC curve on the same tasks. Our model's performance remained above 0.81 area under ROC curve even when evaluated with reduced data. The results support our claim that machine-learning based approaches can lead to reliable and cost-effective forest fire prediction systems.

Improving transient elastography performance for detecting hepatitis B cirrhosis