Biomechanical and Anthropometric Factors That Differentiate National- and Regional-Level Judo Players

2021 ◽  
pp. 1-7
Author(s):  
Felipe Guimarães Teixeira ◽  
Paulo Tadeu Cardozo Ribeiro Rosa ◽  
Roger Gomes Tavares Mello ◽  
Jurandir Nadal
Keyword(s):  
Cross Validation ◽  
Center Of Pressure ◽  
Characteristic Curve ◽  
Multivariable Analysis ◽  
Stepwise Logistic Regression ◽  
Mean Velocity ◽  
Bone Width ◽  
Longitudinal Length ◽  
Judo Athletes ◽  
Fold Cross Validation

Purpose: The study aimed to identify the variables that differentiate judo athletes at national and regional levels. Multivariable analysis was applied to biomechanical, anthropometric, and Special Judo Fitness Test (SJFT) data. Method: Forty-two male judo athletes from 2 competitive groups (14 national and 28 state levels) performed the following measurements and tests: (1) skinfold thickness, (2) circumference, (3) bone width, (4) longitudinal length, (5) stabilometric tests, (6) dynamometric tests, and (7) SJFT. The variables with significant differences in the Wilcoxon rank-sum test were used in stepwise logistic regression to select those that better separate the groups. The authors considered models with a maximum of 3 variables to avoid overfitting. They used 7-fold cross validation to calculate optimism-corrected measures of model performance. Results: The 3 variables that best differentiated the groups were the epicondylar humerus width, the total number of throws on the SJFT, and the stabilometric mean velocity of the center of pressure in the mediolateral direction. The area under the receiver-operating-characteristic curve for the model (based on 7-fold cross validation) was 0.95. Conclusion: This study suggests that a reduced set of anthropometric, biomechanical, and SJFT variables can differentiate judo athlete’s levels.

scholarly journals Quantitative EEG predicts outcomes in children after cardiac arrest

Neurology ◽  
2019 ◽  
Vol 92 (20) ◽  
pp. e2329-e2338 ◽  
Author(s):  
Seungha Lee ◽  
Xuelong Zhao ◽  
Kathryn A. Davis ◽  
Alexis A. Topjian ◽  
Brian Litt ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Predictive Value ◽  
Cross Validation ◽  
Characteristic Curve ◽  
Cerebral Performance Category ◽  
Pediatric Intensive Care ◽  
Quantitative Eeg ◽  
Line Length ◽  
Neurologic Outcomes ◽  
Fold Cross Validation

ObjectiveTo determine whether quantitative EEG (QEEG) features predict neurologic outcomes in children after cardiac arrest.MethodsWe performed a single-center prospective observational study of 87 consecutive children resuscitated and admitted to the pediatric intensive care unit after cardiac arrest. Full-array conventional EEG data were obtained as part of clinical management. We computed 8 QEEG features from 5-minute epochs every hour after return of circulation. We developed predictive models utilizing random forest classifiers trained on patient age and 8 QEEG features to predict outcome. The features included SD of each EEG channel, normalized band power in alpha, beta, theta, delta, and gamma wave frequencies, line length, and regularity function scores. We measured outcomes using Pediatric Cerebral Performance Category (PCPC) scores. We evaluated the models using 5-fold cross-validation and 1,000 bootstrap samples.ResultsThe best performing model had a 5-fold cross-validation accuracy of 0.8 (0.88 area under the receiver operating characteristic curve). It had a positive predictive value of 0.79 and a sensitivity of 0.84 in predicting patients with favorable outcomes (PCPC score of 1–3). It had a negative predictive value of 0.8 and a specificity of 0.75 in predicting patients with unfavorable outcomes (PCPC score of 4–6). The model also identified the relative importance of each feature. Analyses using only frontal electrodes did not differ in prediction performance compared to analyses using all electrodes.ConclusionsQEEG features can standardize EEG interpretation and predict neurologic outcomes in children after cardiac arrest.

Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach

2020 ◽  
Author(s):  
Yu Zhang ◽  
Cangzhi Jia ◽  
Chee Keong Kwoh
Keyword(s):  
Deep Learning ◽  
Molecular Mechanisms ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Characteristic Curve ◽  
Published Data ◽  
Different Types ◽  
Precision Recall Curve ◽  
Deep Learning Model ◽  
Fold Cross Validation

Abstract Long noncoding RNAs (lncRNAs) play significant roles in various physiological and pathological processes via their interactions with biomolecules like DNA, RNA and protein. The existing in silico methods used for predicting the functions of lncRNA mainly rely on calculating the similarity of lncRNA or investigating whether an lncRNA can interact with a specific biomolecule or disease. In this work, we explored the functions of lncRNA from a different perspective: we presented a tool for predicting the interaction biomolecule type for a given lncRNA. For this purpose, we first investigated the main molecular mechanisms of the interactions of lncRNA–RNA, lncRNA–protein and lncRNA–DNA. Then, we developed an ensemble deep learning model: lncIBTP (lncRNA Interaction Biomolecule Type Prediction). This model predicted the interactions between lncRNA and different types of biomolecules. On the 5-fold cross-validation, the lncIBTP achieves average values of 0.7042 in accuracy, 0.7903 and 0.6421 in macro-average area under receiver operating characteristic curve and precision–recall curve, respectively, which illustrates the model effectiveness. Besides, based on the analysis of the collected published data and prediction results, we hypothesized that the characteristics of lncRNAs that interacted with DNA may be different from those that interacted with only RNA.

scholarly journals SAT-547 Aldosterone-Potassium Ratio Predicts Primary Aldosteronism Subtype

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Troy Puar ◽  
Wann Jia Loh ◽  
Dawn Shao Ting Lim ◽  
Meifen Zhang ◽  
Roger S Foo ◽  
...  
Keyword(s):  
Primary Aldosteronism ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Multivariable Analysis ◽  
Predictive Ability ◽  
Cost Effective ◽  
Stepwise Logistic Regression ◽  
Stepwise Logistic Regression Analysis ◽  
Bilateral Disease ◽  
Unilateral Disease

Abstract Objective Prediction models have been developed to predict either unilateral or bilateral primary aldosteronism, and these have not been validated externally. We aimed to develop a simplified score to predict both subtypes and validate this externally. Methods Our development cohort was taken from 165 patients who underwent adrenal vein sampling (AVS) in two Asian tertiary centres. Unilateral disease was determined using both AVS and post-operative outcome. Multivariable analysis was used to construct prediction models. We validated our tool in a European cohort of 97 patients enrolled in a clinical trial. Previously published prediction models were also tested in our cohorts. Results Backward stepwise logistic regression analysis yielded a final tool using baseline-aldosterone-to-lowest-potassium ratio (APR, ng/dL/mmol/L), with an area under receiver operating characteristic curve of 0.80 (95% CI: 0.70 - 0.89). In the Asian development cohort, probability of bilateral disease was 90.0% (with APR <5) and probability of unilateral disease was 91.4% (with APR >15). Similar results were seen in the European validation cohort. Combining both cohorts, probability of bilateral disease was 76.7% (with APR <5), and probability for unilateral was 91.7% (with APR >15). Other models had similar predictive ability but required more variables, and were less sensitive for identifying bilateral PA. Conclusion The novel aldosterone-potassium ratio (APR) is a convenient score to guide clinicians and patients of various ethnicities on the probability of PA subtype. Using APR to identify patients more likely to benefit from AVS may be a cost-effective strategy to manage this common condition.

WRAPPER-BASED FEATURE RANKING TECHNIQUES FOR DETERMINING RELEVANCE OF SOFTWARE ENGINEERING METRICS

2010 ◽  
Vol 17 (05) ◽  
pp. 425-464 ◽  
Author(s):  
WILKER ALTIDOR ◽  
TAGHI M. KHOSHGOFTAAR ◽  
KEHAN GAO
Keyword(s):  
Feature Selection ◽  
Software Engineering ◽  
Software Metrics ◽  
Cross Validation ◽  
Performance Metrics ◽  
Characteristic Curve ◽  
Feature Ranking ◽  
Class Label ◽  
Prediction Problems ◽  
Fold Cross Validation

Classification, an important data mining function that assigns class label to items in a collection, is of practical applications in various domains. In software engineering, for instance, a common classification problem is to determine the quality of a software item. In such a problem, software metrics represent the independent features while the fault proneness represents the class label. With many classification problems, one must often deal with the presence of irrelevant features in the feature space. That, coupled with class imbalance, renders the task of discriminating one class from another rather difficult. In this study, we empirically evaluate our proposed wrapper-based feature ranking where nine performance metrics aided by a particular learner and a methodology are considered. We examine five learners and take three different approaches, each in conjunction with one of three different methodologies: 3-fold Cross-Validation, 3-fold Cross-Validation Risk Impact, and a combination of the two. In this study, we consider two sets of software engineering datasets. To evaluate the classifier performance after feature selection has been applied, we use Area Under Receiver Operating Characteristic curve as the performance evaluator. We investigate the performance of feature selection as we vary the three factors that form the foundation of the wrapper-based feature ranking. We show that the performance is conditioned by not only the choice of methodology but also the learner. We also evaluate the effect of sampling on wrapper-based feature ranking. Finally, we provide guidance as to which software metrics are relevant in software defect prediction problems and how the number of software metrics can be selected when using wrapper-based feature ranking.

scholarly journals Stacked Generalization: An Introduction to Super Learning

2017 ◽  
Author(s):  
Ashley I. Naimi ◽  
Laura B. Balzer
Keyword(s):  
Operating Characteristic ◽  
Cross Validation ◽  
Mean Squared Error ◽  
Characteristic Curve ◽  
Squared Error ◽  
Stacked Generalization ◽  
Super Learner ◽  
Technical Details ◽  
Fold Cross Validation ◽  
Weighted Combination

AbstractStacked generalization is an ensemble method that allows researchers to combine several different prediction algorithms into one. Since its introduction in the early 1990s, the method has evolved several times into what is now known as “Super Learner”. Super Learner uses V -fold cross-validation to build the optimal weighted combination of predictions from a library of candidate algorithms. Optimality is defined by a user-specified objective function, such as minimizing mean squared error or maximizing the area under the receiver operating characteristic curve. Although relatively simple in nature, use of the Super Learner by epidemiologists has been hampered by limitations in understanding conceptual and technical details. We work step-by-step through two examples to illustrate concepts and address common concerns.

scholarly journals LE-MDCAP: A Computational Model to Prioritize Causal miRNA–Disease Associations

2021 ◽  
Vol 22 (24) ◽  
pp. 13607
Author(s):  
Zhou Huang ◽  
Yu Han ◽  
Leibo Liu ◽  
Qinghua Cui ◽  
Yuan Zhou
Keyword(s):  
Computational Models ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Characteristic Curve ◽  
Levenshtein Distance ◽  
Similarity Matrix ◽  
Disease Treatment ◽  
Independent Test ◽  
Disease Associations ◽  
Fold Cross Validation

MicroRNAs (miRNAs) are associated with various complex human diseases and some miRNAs can be directly involved in the mechanisms of disease. Identifying disease-causative miRNAs can provide novel insight in disease pathogenesis from a miRNA perspective and facilitate disease treatment. To date, various computational models have been developed to predict general miRNA–disease associations, but few models are available to further prioritize causal miRNA–disease associations from non-causal associations. Therefore, in this study, we constructed a Levenshtein-Distance-Enhanced miRNA–Disease Causal Association Predictor (LE-MDCAP), to predict potential causal miRNA–disease associations. Specifically, Levenshtein distance matrixes covering the sequence, expression and functional miRNA similarities were introduced to enhance the previous Gaussian interaction profile kernel-based similarity matrix. LE-MDCAP integrated miRNA similarity matrices, disease semantic similarity matrix and known causal miRNA–disease associations to make predictions. For regular causal vs. non-disease association discrimination task, LF-MDCAP achieved area under the receiver operating characteristic curve (AUROC) of 0.911 and 0.906 in 10-fold cross-validation and independent test, respectively. More importantly, LE-MDCAP prominently outperformed the previous MDCAP model in distinguishing causal versus non-causal miRNA–disease associations (AUROC 0.820 vs. 0.695). Case studies performed on diabetic retinopathy and hsa-mir-361 also validated the accuracy of our model. In summary, LE-MDCAP could be useful for screening causal miRNA–disease associations from general miRNA–disease associations.

The Role of a Decision Rule in Symptomatic Pulmonary Embolism Patients with a Non-high Probability Ventilation-perfusion Scan

1997 ◽  
Vol 78 (02) ◽  
pp. 794-798 ◽  
Author(s):  
Bowine C Michel ◽  
Philomeen M M Kuijer ◽  
Joseph McDonnell ◽  
Edwin J R van Beek ◽  
Frans F H Rutten ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Decision Rule ◽  
Predictive Value ◽  
Patient Population ◽  
High Probability ◽  
Characteristic Curve ◽  
Stepwise Logistic Regression ◽  
Pulmonary Angiogram ◽  
Symptomatic Pulmonary Embolism ◽  
Perfusion Scan

Summary Background: In order to improve the use of information contained in the medical history and physical examination in patients with suspected pulmonary embolism and a non-high probability ventilation-perfusion scan, we assessed whether a simple, quantitative decision rule could be derived for the diagnosis or exclusion of pulmonary embolism. Methods: In 140 consecutive symptomatic patients with a non- high probability ventilation-perfusion scan and an interpretable pulmonary angiogram, various clinical and lung scan items were collected prospectively and analyzed by multivariate stepwise logistic regression analysis to identify the most informative combination of items. Results: The prevalence of proven pulmonary embolism in the patient population was 27.1%. A decision rule containing the presence of wheezing, previous deep venous thrombosis, recently developed or worsened cough, body temperature above 37° C and multiple defects on the perfusion scan was constructed. For the rule the area under the Receiver Operating Characteristic curve was larger than that of the prior probability of pulmonary embolism as assessed by the physician at presentation (0.76 versus 0.59; p = 0.0097). At the cut-off point with the maximal positive predictive value 2% of the patients scored positive, at the cut-off point with the maximal negative predictive value pulmonary embolism could be excluded in 16% of the patients. Conclusions: We derived a simple decision rule containing 5 easily interpretable variables for the patient population specified. The optimal use of the rule appears to be in the exclusion of pulmonary embolism. Prospective validation of this rule is indicated to confirm its clinical utility.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

Rancang Bangun Sistem Informasi Untuk Menentukan Kapabilitas Konsumen Dalam Mengambil Pinjaman KPR

2016 ◽  
Vol 7 (2) ◽  
pp. 75-80
Author(s):  
Adhi Kusnadi ◽  
Risyad Ananda Putra
Keyword(s):  
Data Mining ◽  
Low Income ◽  
Cross Validation ◽  
Classification Tree ◽  
Large Population ◽  
Housing Development ◽  
Good Precision ◽  
Index Terms ◽  
The Government ◽  
Fold Cross Validation

Indonesia is one country that has a relatively large population . The government in the period of 5 years, annually hold a procurement program 1 million FLPP house units. This program is held in an effort to provide a decent home for low income people. FLPP housing development requires good precision and speed of development on the part of the developer, this is often hampered by the bank process, because it is difficult to predict the results and speed of data processing in the bank. Knowing the ability of consumers to get subsidized credit, has many advantages, among others, developers can plan a better cash flow, and developers can replace consumers who will be rejected before entering the bank process. For that reason built a system that can help developers. There are many methods that can be used to create this application. One of them is data mining with Classification tree. The results of 10-fold-cross-validation applications have an accuracy of 92%. Index Terms-Data Mining, Classification Tree, Housing, FLPP, 10-fold-cross Validation, Consumer Capability

Klasifikasi Berita Kriminal Menggunakan Naïve Bayes Classifier (NBC) dengan Pengujian K-Fold Cross Validation

2019 ◽  
Vol 5 (2) ◽  
pp. 108-117
Author(s):  
Herfia Rhomadhona ◽  
Jaka Permadi
Keyword(s):  
Cross Validation ◽  
Online Media ◽  
Bayes Classifier ◽  
Ve Bayes ◽  
Fold Cross Validation

Berita kriminalitas merupakan berita yang selalu menjadi trending topik di setiap media massa, khususnya media massa online. Media massa online terlah menyediakan beberapa fasilitas untuk mempermudah masyarakan dalam mencari sebuah berita berdasarkan topik. Media massa online melabeli suatu berita berdasarkan kategorinya. Namun, media massa online tidak memberikan sub kategori pada berita tersebut. Sebagai contoh jika seorang pengguna membuka kategori kriminal, maka yang ditampilkan adalah semua jenis berita kriminal tanpa memberikan informasi yang spesifik dari jenis kriminalitasnya. Permasalahan tersebut dapat diatasi dengan mengklasifikasikan berita kriminalitas berdasarkan subkategori. Penelitian ini menggunakan metode Naïve Bayes Classifier (NBC)  untuk mengklasifikasi berita berdasarkan sub kategorinya. Adapun subkategori terbagi kedalam 5 kategori yaitu korupsi, narkoba, pencurian, pemerkosaan dan pembunuhan. Penelitian ini bertujuan untuk mengetahui kemampuan NBC dalam mengklasifikasi berita dengan melakukan pengujian menggunakan teknik K-Fold Cross Validation dengan nilai K dari 3 sampai 10. Hasil pengujian menyatakan bahwa NBC memiliki kemampuan dalam klasifikasi berita kriminal dengan nilai precision sebesar 98,53 %, nilai recall sebesar 98,44 % dan nilai accuracy sebesar 99,38 %.

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