scholarly journals The Novel Quantitative Technique for Assessment of Gait Symmetry Using Advanced Statistical Learning Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Jianning Wu ◽  
Bin Wu
Keyword(s):  
Statistical Learning ◽  
Learning Algorithm ◽  
Lower Limbs ◽  
Classification Method ◽  
Support Vector ◽  
Accurate Identification ◽  
Index Method ◽  
Gait Symmetry ◽  
Significant Difference ◽  
Gait Asymmetry

The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.

scholarly journals Multifactor Prediction of Embryo Transfer Outcomes Based on a Machine Learning Algorithm

2021 ◽  
Vol 12 ◽  
Author(s):  
Ran Liu ◽  
Shun Bai ◽  
Xiaohua Jiang ◽  
Lihua Luo ◽  
Xianhong Tong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Embryo Transfer ◽  
Learning Algorithm ◽  
Conditional Inference ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Significant Difference ◽  
Predictive Rate ◽  
Ivf Et ◽  
Day Of Embryo Transfer

In vitro fertilization-embryo transfer (IVF-ET) technology make it possible for infertile couples to conceive a baby successfully. Nevertheless, IVF-ET does not guarantee success. Frozen embryo transfer (FET) is an important supplement to IVF-ET. Many factors are correlated with the outcome of FET which is unpredictable. Machine learning is a field of study that predict various outcomes by defining data attributes and using relevant data and calculation algorithms. Machine learning algorithm has been widely used in clinical research. The present study focuses on making predictions of early pregnancy outcomes in FET through clinical characters, including age, body mass index (BMI), endometrial thickness (EMT) on the day of progesterone treatment, good-quality embryo rate (GQR), and type of infertility (primary or secondary), serum estradiol level (E2) on the day of embryo transfer, and serum progesterone level (P) on the day of embryo transfer. We applied four representative machine learning algorithms, including logistic regression (LR), conditional inference tree, random forest (RF) and support vector machine (SVM) to build prediction models and identify the predictive factors. We found no significant difference among the models in the sensitivity, specificity, positive predictive rate, negative predictive rate or accuracy in predicting the pregnancy outcome of FET. For example, the positive/negative predictive rate of the SVM (gamma = 1, cost = 100, 10-fold cross validation) is 0.56 and 0.55. This approach could provide a reference for couples considering FET. The prediction accuracy of the present study is limited, which suggests that there may be some other more effective predictors to be developed in future work.

scholarly journals Classification and prediction of protein–protein interaction interface using machine learning algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Subhrangshu Das ◽  
Saikat Chakrabarti
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Learning Algorithm ◽  
Protein Complexes ◽  
Three Dimensional ◽  
3D Models ◽  
Structural Features ◽  
Support Vector ◽  
Protein Protein Interaction ◽  
Significant Difference

AbstractStructural insight of the protein–protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therapeutic target. However, owing to experimental lag in solving protein–protein complex structures, three-dimensional (3D) knowledge of the PPI interfaces can be gained via computational approaches like molecular docking and post-docking analyses. Despite development of numerous docking tools and techniques, success in identification of native like interfaces based on docking score functions is limited. Hence, we employed an in-depth investigation of the structural features of the interface that might successfully delineate native complexes from non-native ones. We identify interface properties, which show statistically significant difference between native and non-native interfaces belonging to homo and hetero, protein–protein complexes. Utilizing these properties, a support vector machine (SVM) based classification scheme has been implemented to differentiate native and non-native like complexes generated using docking decoys. Benchmarking and comparative analyses suggest very good performance of our SVM classifiers. Further, protein interactions, which are proven via experimental findings but not resolved structurally, were subjected to this approach where 3D-models of the complexes were generated and most likely interfaces were predicted. A web server called Protein Complex Prediction by Interface Properties (PCPIP) is developed to predict whether interface of a given protein–protein dimer complex resembles known protein interfaces. The server is freely available at http://www.hpppi.iicb.res.in/pcpip/.

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

scholarly journals Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

2021 ◽  
Vol 10 (5) ◽  
pp. 992
Author(s):  
Martina Barchitta ◽  
Andrea Maugeri ◽  
Giuliana Favara ◽  
Paolo Marco Riela ◽  
Giovanni Gallo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Support Vector ◽  
Icu Admission ◽  
Risk Of Death ◽  
Saps Ii ◽  
Svm Algorithm

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients’ characteristics at ICU admission. We used data from the “Italian Nosocomial Infections Surveillance in Intensive Care Units” network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient’s origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

scholarly journals Machine learning-based ability to classify psychosis and early stages of disease through parenting and attachment-related variables is associated with social cognition

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Linda A. Antonucci ◽  
Alessandra Raio ◽  
Giulio Pergola ◽  
Barbara Gelao ◽  
Marco Papalino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Cognition ◽  
Attachment Style ◽  
Learning Algorithm ◽  
Early Recognition ◽  
Emotion Perception ◽  
First Episode ◽  
Support Vector ◽  
Significant Group ◽  
Early Stages

Abstract Background Recent views posited that negative parenting and attachment insecurity can be considered as general environmental factors of vulnerability for psychosis, specifically for individuals diagnosed with psychosis (PSY). Furthermore, evidence highlighted a tight relationship between attachment style and social cognition abilities, a key PSY behavioral phenotype. The aim of this study is to generate a machine learning algorithm based on the perceived quality of parenting and attachment style-related features to discriminate between PSY and healthy controls (HC) and to investigate its ability to track PSY early stages and risk conditions, as well as its association with social cognition performance. Methods Perceived maternal and paternal parenting, as well as attachment anxiety and avoidance scores, were trained to separate 71 HC from 34 PSY (20 individuals diagnosed with schizophrenia + 14 diagnosed with bipolar disorder with psychotic manifestations) using support vector classification and repeated nested cross-validation. We then validated this model on independent datasets including individuals at the early stages of disease (ESD, i.e. first episode of psychosis or depression, or at-risk mental state for psychosis) and with familial high risk for PSY (FHR, i.e. having a first-degree relative suffering from psychosis). Then, we performed factorial analyses to test the group x classification rate interaction on emotion perception, social inference and managing of emotions abilities. Results The perceived parenting and attachment-based machine learning model discriminated PSY from HC with a Balanced Accuracy (BAC) of 72.2%. Slightly lower classification performance was measured in the ESD sample (HC-ESD BAC = 63.5%), while the model could not discriminate between FHR and HC (BAC = 44.2%). We observed a significant group x classification interaction in PSY and HC from the discovery sample on emotion perception and on the ability to manage emotions (both p = 0.02). The interaction on managing of emotion abilities was replicated in the ESD and HC validation sample (p = 0.03). Conclusion Our results suggest that parenting and attachment-related variables bear significant classification power when applied to both PSY and its early stages and are associated with variability in emotion processing. These variables could therefore be useful in psychosis early recognition programs aimed at softening the psychosis-associated disability.

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

Computed tomography angiography and microsurgical flaps for traumatic wounds: What is the added value?

2021 ◽  
pp. 1-9
Author(s):  
Lucas Sousa Macedo ◽  
Renato Polese Rusig ◽  
Gustavo Bersani Silva ◽  
Alvaro Baik Cho ◽  
Teng Hsiang Wei ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Upper Limb ◽  
Computed Tomography Angiography ◽  
Lower Limb ◽  
High Energy ◽  
Lower Limbs ◽  
Added Value ◽  
Vascular Lesions ◽  
Limb Injuries ◽  
Significant Difference

BACKGROUND: Microsurgical flaps are widely used to treat complex traumatic wounds of upper and lower limbs. Few studies have evaluated whether the vascular changes in preoperative computed tomography angiography (CTA) influence the selection of recipient vessel and type of anastomosis and the microsurgical flaps outcomes including complications. OBJECTIVE: The aim of this study was to evaluate if preoperative CTA reduces the occurrence of major complications (revision of the anastomosis, partial or total flap failure, and amputation) of the flaps in upper and lower limb trauma, and to describe and analyze the vascular lesions of the group with CTA and its relationship with complications. METHODS: A retrospective cohort study was undertaken with all 121 consecutive patients submitted to microsurgical flaps for traumatic lower and upper limb, from 2014 to 2020. Patients were divided into two groups: patients with preoperative CTA (CTA+) and patients not submitted to CTA (CTA–). The presence of postoperative complications was assessed and, within CTA+, we also analyzed the number of patent arteries on CTA and described the arterial lesions. RESULTS: Of the 121 flaps evaluated (84 in the lower limb and 37 in the upper limb), 64 patients underwent preoperative CTA. In the CTA+ group, 56% of patients with free flaps for lower limb had complete occlusion of one artery. CTA+ patients had a higher rate of complications (p = 0.031), which may represent a selection bias as the most complex limb injuries and may have CTA indicated more frequently. The highest rate of complications was observed in chronic cases (p = 0.034). There was no statistically significant difference in complications in patients with preoperative vascular injury or the number of patent arteries. CONCLUSIONS: CTA should not be performed routinely, however, CTA may help in surgical planning, especially in complex cases of high-energy and chronic cases, since it provides information on the best recipient artery and the adequate level to perform the microanastomosis, outside the lesion area.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

scholarly journals Plantar load transfer in children: a descriptive study with two pathological case studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alexis Brierty ◽  
Christopher P. Carty ◽  
Claudia Giacomozzi ◽  
Teresa Phillips ◽  
Henry P. J. Walsh ◽  
...  
Keyword(s):  
Case Studies ◽  
Load Transfer ◽  
Descriptive Study ◽  
Transfer Phase ◽  
Gait Symmetry ◽  
Plantar Pressures ◽  
Gait Asymmetry ◽  
Mean Pressure ◽  
Pathological Case

Abstract Background Typical gait is often considered to be highly symmetrical, with gait asymmetries typically associated with pathological gait. Whilst gait symmetry is often expressed in symmetry ratios, measures of symmetry do not provide insight into how these asymmetries affect gait variables. To fully understand changes caused by gait asymmetry, we must first develop a normative database for comparison. Therefore, the aim of this study was to describe normative reference values of regional plantar load and present comparisons with two pathological case studies. Methods A descriptive study of the load transfer of plantar pressures in typically developed children was conducted to develop a baseline for comparison of the effects of gait asymmetry in paediatric clinical populations. Plantar load and 3D kinematic data was collected for 17 typically developed participants with a mean age of 9.4 ± 4.0 years. Two case studies were also included; a 10-year-old male with clubfoot and an 8-year-old female with a flatfoot deformity. Data was analysed using a kinematics-pressure integration technique for anatomical masking into 5 regions of interest; medial and lateral forefoot, midfoot, and medial and lateral hindfoot. Results Clear differences between the two case studies and the typical dataset were seen for the load transfer phase of gait. For case study one, lateral bias was seen in the forefoot of the trailing foot across all variables, as well as increases in contact area, force and mean pressure in the lateral hindfoot of the leading foot. For case study two, the forefoot of the trailing foot produced results very similar to the typical dataset across all variables. In the hindfoot of the leading foot, medial bias presents most notably in the force and mean pressure graphs. Conclusions This study highlights the clinical significance of the load transfer phase of gait, providing meaningful information for intervention planning.

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