scholarly journals Knee kinematic curve representation and application to knee pathology classification

2018 ◽  
Vol 4 (1) ◽  
pp. 32 ◽  
Author(s):  
Badreddine Ben Nouma ◽  
Amar Mitiche ◽  
Youssef Ouakrim ◽  
Neila Mezghani
Keyword(s):  
Variational Method ◽  
Support Vector ◽  
Single Individual ◽  
Knee Kinematic ◽  
Classification Problems ◽  
Essential Information ◽  
Pathology Classification ◽  
Curve Representation ◽  
Knee Pathology ◽  
Corrected Curve

This study investigates a variational method to determine the most representative shape of a set of knee kinematic curves with application to knee pathology classification. Although they provide essential information for pathology classification, knee kinematic curves are characterized by high intra-class variability and outliers are often present. As a result, a set of several measurement curves are acquired of any single individual which are then averaged before their use for pathology classification. Rather than using the average of an individual’s recorded measurement curves, this method determines a better representative curve by first correcting the data to account for outliers occurrence and class variability using a variational method. The correction is performed by simultaneous minimization of a set of objective functions, one for each curve in the measurement set, and consisting of a weighed sum of two terms: a data term of conformity of the corrected curve to the given curve, and a regularization term of proximity of the corrected curve to the mean of all the corrected curves to inhibit the influence of outliers in the set. Validation tests were performed to discriminate between knee osteoarthritis data (OA) and non-OA data. Using a support vector machine, the classification accuracy with the proposed representation was 86%, with 81% sensitivity and 90% specificity, compared to 83% accuracy for the standard representation by average, with 76% sensitivity and 90% specificity. The representation has also been tested within the OA category to distinguish the femero-tibial patholgy from the femero-patellar, giving 76% accuracy, with 76% sensitivity and 76% specificity, compared to 69% accuracy, with 62% sensitivity and 76% specificity. These significant improvements by the proposed method warrant its further investigation by application to other biomedical engineering pattern classification problems and datasets.

scholarly journals A Sample-Encoding Generalization of the Kohonen Associative Memory and Application to Knee Kinematic Data Representation and Pathology Classification

2019 ◽  
Vol 9 (9) ◽  
pp. 1741 ◽  
Author(s):  
Badreddine Ben Nouma ◽  
Amar Mitiche ◽  
Neila Mezghani
Keyword(s):  
Three Dimensional ◽  
Data Representation ◽  
Small Sample ◽  
Repeated Measurements ◽  
Training Data ◽  
Multidimensional Data ◽  
Knee Kinematic ◽  
Kinematic Data ◽  
Pathology Classification ◽  
Knee Pathology

Knee kinematic data consist of a small sample of high-dimensional vectors recording repeated measurements of the temporal variation of each of the three fundamental angles of knee three-dimensional rotation during a walking cycle. In applications such as knee pathology classification, the notorious problems of high-dimensionality (the curse of dimensionality), high intra-class variability, and inter-class similarity make this data generally difficult to interpret. In the face of these difficulties, the purpose of this study is to investigate knee kinematic data classification by a Kohonen neural network generalized to encode samples of multidimensional data vectors rather than single such vectors as in the standard network. The network training algorithm and its ensuing classification function both use the Hotelling T 2 statistic to evaluate the underlying sample similarity, thus affording efficient use of training data for network development and robust classification of observed data. Applied to knee osteoarthritis pathology discrimination, namely the femoro-rotulian (FR) and femoro-tibial (FT) categories, the scheme improves on the state-of-the-art methods.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

scholarly journals Kernel Based Data-Adaptive Support Vector Machines for Multi-Class Classification

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 936
Author(s):  
Jianli Shao ◽  
Xin Liu ◽  
Wenqing He
Keyword(s):  
Machine Learning ◽  
Spatial Association ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Real Data ◽  
Kernel Functions ◽  
Support Vector ◽  
Classification Problems ◽  
Rare Class ◽  
Data Adaptive

Imbalanced data exist in many classification problems. The classification of imbalanced data has remarkable challenges in machine learning. The support vector machine (SVM) and its variants are popularly used in machine learning among different classifiers thanks to their flexibility and interpretability. However, the performance of SVMs is impacted when the data are imbalanced, which is a typical data structure in the multi-category classification problem. In this paper, we employ the data-adaptive SVM with scaled kernel functions to classify instances for a multi-class population. We propose a multi-class data-dependent kernel function for the SVM by considering class imbalance and the spatial association among instances so that the classification accuracy is enhanced. Simulation studies demonstrate the superb performance of the proposed method, and a real multi-class prostate cancer image dataset is employed as an illustration. Not only does the proposed method outperform the competitor methods in terms of the commonly used accuracy measures such as the F-score and G-means, but also successfully detects more than 60% of instances from the rare class in the real data, while the competitors can only detect less than 20% of the rare class instances. The proposed method will benefit other scientific research fields, such as multiple region boundary detection.

scholarly journals A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 27789-27801 ◽  
Author(s):  
Hongxin Xue ◽  
Yanping Bai ◽  
Hongping Hu ◽  
Ting Xu ◽  
Haijian Liang
Keyword(s):  
Support Vector Machine ◽  
Hybrid Model ◽  
Support Vector ◽  
Classification Problems ◽  
Model Based

scholarly journals A Novel Weighted Support Vector Machine Based on Particle Swarm Optimization for Gene Selection and Tumor Classification

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Mohammad Javad Abdi ◽  
Seyed Mohammad Hosseini ◽  
Mansoor Rezghi
Keyword(s):  
Particle Swarm Optimization ◽  
Dna Microarrays ◽  
Gene Selection ◽  
Particle Swarm ◽  
Tumor Classification ◽  
Support Vector ◽  
Classification Problems ◽  
Target Class ◽  
Swarm Optimization ◽  
Kernel Parameters

We develop a detection model based on support vector machines (SVMs) and particle swarm optimization (PSO) for gene selection and tumor classification problems. The proposed model consists of two stages: first, the well-known minimum redundancy-maximum relevance (mRMR) method is applied to preselect genes that have the highest relevance with the target class and are maximally dissimilar to each other. Then, PSO is proposed to form a novel weighted SVM (WSVM) to classify samples. In this WSVM, PSO not only discards redundant genes, but also especially takes into account the degree of importance of each gene and assigns diverse weights to the different genes. We also use PSO to find appropriate kernel parameters since the choice of gene weights influences the optimal kernel parameters and vice versa. Experimental results show that the proposed mRMR-PSO-WSVM model achieves highest classification accuracy on two popular leukemia and colon gene expression datasets obtained from DNA microarrays. Therefore, we can conclude that our proposed method is very promising compared to the previously reported results.

Shape-restricted support vector machine (SR-SVM): a SVM classifier taking supplementary shape information of input

2021 ◽  
Vol 40 (1) ◽  
pp. 1481-1494
Author(s):  
Geng Deng ◽  
Yaoguo Xie ◽  
Xindong Wang ◽  
Qiang Fu
Keyword(s):  
Support Vector Machine ◽  
Classification Performance ◽  
Research Literature ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Problems ◽  
Active Set ◽  
Shape Information ◽  
Convex Optimization Problem ◽  
Shape Restrictions

Many classification problems contain shape information from input features, such as monotonic, convex, and concave. In this research, we propose a new classifier, called Shape-Restricted Support Vector Machine (SR-SVM), which takes the component-wise shape information to enhance classification accuracy. There exists vast research literature on monotonic classification covering monotonic or ordinal shapes. Our proposed classifier extends to handle convex and concave types of features, and combinations of these types. While standard SVM uses linear separating hyperplanes, our novel SR-SVM essentially constructs non-parametric and nonlinear separating planes subject to component-wise shape restrictions. We formulate SR-SVM classifier as a convex optimization problem and solve it using an active-set algorithm. The approach applies basis function expansions on the input and effectively utilizes the standard SVM solver. We illustrate our methodology using simulation and real world examples, and show that SR-SVM improves the classification performance with additional shape information of input.

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