HIERARCHICAL ANALYSIS AND CLASSIFICATION OF ASYMPTOMATIC AND KNEE OSTEOARTHRITIS GAIT PATTERNS USING A WAVELET REPRESENTATION OF KINETIC DATA AND THE NEAREST NEIGHBOR CLASSIFIER

2008 ◽  
Vol 08 (01) ◽  
pp. 45-54 ◽  
Author(s):  
NEILA MEZGHANI ◽  
KARINE BOIVIN ◽  
KATIA TURCOT ◽  
RACHID AISSAOUI ◽  
NICOLA HAGMEISTER ◽  
...  
Keyword(s):  
Knee Osteoarthritis ◽  
Nearest Neighbor ◽  
Reaction Force ◽  
Hierarchical Classification ◽  
Gait Patterns ◽  
Knee Oa ◽  
Nearest Neighbor Rule ◽  
Kellgren And Lawrence ◽  
Using Data

The purpose of this study is twofold: (1) to develop a classification method to distinguish between asymptomatic (AS) and knee osteoarthritis (OA) gait patterns using ground reaction force (GRF) measurements, and (2) to investigate OA severity within OA gait patterns. Features were first extracted from the GRF vectors to be used for classification. We investigated a two-level hierarchical classification and analysis method using the nearest neighbor rule. At the first level, the GRF data were classified into two classes: AS and OA. At the second level, the GRF data of OA patients were classified according to the pathology severity. The OA patients were grouped into two OA severity categories according to the Kellgren and Lawrence (KL) scale: KL 1 and KL 2 for one category, and KL 3 and KL 4 for the other. Experiments were conducted using data of 42 cases, 16 AS and 26 pathological. The method discriminated between AS and OA subjects with an accuracy of 38 of 42 cases, and assessed the severity correctly with an accuracy of 20 of 26 cases. These results demonstrated the validity of both, the feature and the classifier, for automatic classification of AS and knee OA gait patterns and for analysis of OA severity.

Crop Disease Detection Using Data Science Techniques

2021 ◽  
pp. 80-97
Author(s):  
Shakti Kumar
Keyword(s):  
Data Science ◽  
Nearest Neighbor ◽  
Plant Diseases ◽  
K Nearest Neighbor ◽  
Classification Feature ◽  
Crop Disease ◽  
Using Data ◽  
Agricultural Yields ◽  
Day By Day

Plant disease is a mutilation of the normal state of a plant that changes its essential quality and prevents a plant from performing to its actual potential. Due to drastic environment changes, plant diseases are growing day by day, which results the higher losses in quantity of agricultural yields. To prevent the loss in the crop yield, the timely disease identification is necessary. Monitoring the plant diseases without any digital mean makes it difficult to identify the disease correctly and timely. It requires more amounts of work, time, and great experience in the plant diseases. Automatic approach of image processing and applying the different data science techniques to classify the disease correctly is a good idea for this which includes acquisition, classification, feature extraction, pre-processing, and segmentation all are performed on the leaf images. This chapter will briefly discuss the data science techniques used for the classification of the images like SVM, k-nearest neighbor, decision tree, ANN, and convolutional neural network (CNN).

scholarly journals Association between meniscal volume and development of knee osteoarthritis

Rheumatology ◽  
2020 ◽  
Author(s):  
Dawei Xu ◽  
Jan van der Voet ◽  
Nils M Hansson ◽  
Stefan Klein ◽  
Edwin H G Oei ◽  
...  
Keyword(s):  
Knee Osteoarthritis ◽  
Primary Outcome ◽  
Joint Space Narrowing ◽  
Joint Space ◽  
Primary Outcome Measure ◽  
Obese Women ◽  
Knee Oa ◽  
Kellgren And Lawrence ◽  
Over Time

Abstract Objective To assess the association between meniscal volume, its change over time and the development of knee OA after 30 months in overweight/obese women. Methods Data from the PRevention of knee Osteoarthritis in Overweight Females study were used. This cohort included 407 women with a BMI ≥ 27 kg/m2, free of OA-related symptoms. The primary outcome measure was incident OA after 30 months, defined by one out of the following criteria: medial or lateral joint space narrowing (JSN)  ≥ 1.0 mm, incident radiographic OA [Kellgren and Lawrence (K&L)  ≥ 2], or incident clinical OA. The secondary outcomes were either of these items separately. Menisci at both baseline and follow-up were automatically segmented to obtain meniscal volume and delta-volumes. Generalized estimating equations were used to evaluate associations between the volume measures and the outcomes. Results Medial and lateral baseline and delta-volumes were not significantly associated to the primary outcome. Lateral meniscal baseline volume was significantly associated to lateral JSN [odds ratio (OR) = 0.87; 95% CI: 0.75, 0.99], while other measures were not. Medial and lateral baseline volume were positively associated to K&L incidence (OR = 1.32 and 1.22; 95% CI: 1.15, 1.50 and 1.03, 1.45, respectively), while medial and lateral delta-volume were negatively associated to K&L incidence (OR = 0.998 and 0.997; 95% CI: 0.997, 1.000 and 0.996, 0.999, respectively). None of the meniscal measures were significantly associated to incident clinical OA. Conclusion Larger baseline meniscal volume and the decrease of meniscal volume over time were associated to the development of structural OA after 30 months in overweight and obese women.

scholarly journals Data Mining Classification Of Filing Credit Customers Without Collateral With K-Nearest Neighbor Algorithm (Case study: PT. BPR Diori Double)

2020 ◽  
Vol 2 (2) ◽  
pp. 204-210
Author(s):  
Jeprianto Sinaga ◽  
Bosker Sinaga
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Savings And Loans ◽  
Long Time ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
The Many

Unsecured loans are the community's choice for lending to banks that provide Reviews These services. PT. RB Diori Ganda is a regional private banking company that serves savings and loans and loans without collateral for the community. Submission of unsecured loans must go through an assessor team to process the analysis of the attributes that Affect the customer's classification so that credit can be approved, the which is then submitted to the commissioner for credit approval. But what if Reviews those who apply for credit on the same day in large amounts, of course this will the make the process of credit analysis and approval will take a long time. If it is seen from the many needs of the community to apply for loans without collateral, a classification application is needed, in order to Facilitate the work of the assessor team in the process of analyzing the attributes that Affect customer classification. To find out the classification of customers who apply for unsecured loans for using data mining with the K-Nearest Neighbor algorithm. The result of this research is the classification of problematic or non-performing customers for credit applications without collateral.

scholarly journals Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

2020 ◽  
Vol 18 (1) ◽  
pp. 75
Author(s):  
M. Jupri ◽  
Riyanarto Sarno
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Fuzzy Ahp ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Time Required ◽  
Mining Algorithms

The achievement of accepting optimal tax need effective and efficient tax supervision can be achieved by classifying taxpayer compliance to tax regulations. Considering this issue, this paper proposes the classification of taxpayer compliance using data mining algorithms; i.e. C4.5, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, and Multilayer Perceptron based on the compliance of taxpayer data. The taxpayer compliance can be classified into four classes, which are (1) formal and material compliant taxpayers, (2) formal compliant taxpayers, (3) material compliant taxpayers, and (4) formal and material non-compliant taxpayers. Furthermore, the results of data mining algorithms are compared by using Fuzzy AHP and TOPSIS to determine the best performance classification based on the criteria of Accuracy, F-Score, and Time required. Selection of the taxpayer's priority for more detailed supervision at each level of taxpayer compliance is ranked using Fuzzy AHP and TOPSIS based on criteria of dataset variables. The results show that C4.5 is the best performance classification and achieves preference value of 0.998; whereas the MLP algorithm results from the lowest preference value of 0.131. Alternative taxpayer A233 is the top priority taxpayer with a preference value of 0.433; whereas alternative taxpayer A051 is the lowest priority taxpayer with a preference value of 0.036.

scholarly journals Multi-label classification of Indonesian news topics using Pseudo Nearest Neighbor Rule

2019 ◽  
Vol 1192 ◽  
pp. 012031
Author(s):  
Reza Agung Pambudi ◽  
Adiwijaya ◽  
Mohamad Syahrul Mubarok
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbor Rule

scholarly journals Automating classification of osteoarthritis according to Kellgren-Lawrence in the knee using deep learning in an unfiltered adult population

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Simon Olsson ◽  
Ehsan Akbarian ◽  
Anna Lind ◽  
Ali Sharif Razavian ◽  
Max Gordon
Keyword(s):  
Deep Learning ◽  
Knee Osteoarthritis ◽  
Adult Population ◽  
Weight Bearing ◽  
Correct Diagnosis ◽  
University Hospital ◽  
Knee Oa ◽  
Orthopedic Surgeons ◽  
Visual Disturbances

Abstract Background Prevalence for knee osteoarthritis is rising in both Sweden and globally due to increased age and obesity in the population. This has subsequently led to an increasing demand for knee arthroplasties. Correct diagnosis and classification of a knee osteoarthritis (OA) are therefore of a great interest in following-up and planning for either conservative or operative management. Most orthopedic surgeons rely on standard weight bearing radiographs of the knee. Improving the reliability and reproducibility of these interpretations could thus be hugely beneficial. Recently, deep learning which is a form of artificial intelligence (AI), has been showing promising results in interpreting radiographic images. In this study, we aim to evaluate how well an AI can classify the severity of knee OA, using entire image series and not excluding common visual disturbances such as an implant, cast and non-degenerative pathologies. Methods We selected 6103 radiographic exams of the knee taken at Danderyd University Hospital between the years 2002-2016 and manually categorized them according to the Kellgren & Lawrence grading scale (KL). We then trained a convolutional neural network (CNN) of ResNet architecture using PyTorch. We evaluated the results against a test set of 300 exams that had been reviewed independently by two senior orthopedic surgeons who settled eventual interobserver disagreements through consensus sessions. Results The CNN yielded an overall AUC of more than 0.87 for all KL grades except KL grade 2, which yielded an AUC of 0.8 and a mean AUC of 0.92. When merging adjacent KL grades, all but one group showed near perfect results with AUC > 0.95 indicating excellent performance. Conclusion We have found that we could teach a CNN to correctly diagnose and classify the severity of knee OA using the KL grading system without cleaning the input data from major visual disturbances such as implants and other pathologies.

scholarly journals Feature Analysis of Smart Shoe Sensors for Classification of Gait Patterns

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6253
Author(s):  
Unang Sunarya ◽  
Yuli Sun Hariyani ◽  
Taeheum Cho ◽  
Jongryun Roh ◽  
Joonho Hyeong ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Pressure Sensors ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Human Gait ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Gait Patterns ◽  
Gait Classification

Gait analysis is commonly used to detect foot disorders and abnormalities such as supination, pronation, unstable left foot and unstable right foot. Early detection of these abnormalities could help us to correct the walking posture and avoid getting injuries. This paper presents extensive feature analyses on smart shoes sensor data, including pressure sensors, accelerometer and gyroscope signals, to obtain the optimum combination of the sensors for gait classification, which is crucial to implement a power-efficient mobile smart shoes system. In addition, we investigated the optimal length of data segmentation based on the gait cycle parameters, reduction of the feature dimensions and feature selection for the classification of the gait patterns. Benchmark tests among several machine learning algorithms were conducted using random forest, k-nearest neighbor (KNN), logistic regression and support vector machine (SVM) algorithms for the classification task. Our experiments demonstrated the combination of accelerometer and gyroscope sensor features with SVM achieved the best performance with 89.36% accuracy, 89.76% precision and 88.44% recall. This research suggests a new state-of-the-art gait classification approach, specifically on detecting human gait abnormalities.

scholarly journals Biomechanical Alterations during Sit-to-Stand Transfer Are Caused by a Synergy between Knee Osteoarthritis and Obesity

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Loek Verlaan ◽  
Ramon J. Boekesteijn ◽  
Pieter W. Oomen ◽  
Wai-Yan Liu ◽  
Marloes J. M. Peters ◽  
...  
Keyword(s):  
Knee Osteoarthritis ◽  
Reaction Force ◽  
Three Dimensional ◽  
Movement Analysis ◽  
Healthy Controls ◽  
Compensatory Mechanisms ◽  
Vertical Ground Reaction Force ◽  
Knee Oa ◽  
Sit To Stand ◽  
Current Prevalence

Osteoarthritis is one of the major causes of immobility and its current prevalence in elderly (>60 years) is 18% in women and 9.6% in men. Patients with osteoarthritis display altered movement patterns to avoid pain and overcome movement limitations in activities of daily life, such as sit-to-stand transfers. Currently, there is a lack of evidence that distinguishes effects of knee osteoarthritis on sit-to-stand performance in patients with and without obesity. The purpose of this study was therefore to investigate differences in knee and hip kinetics during sit-to-stand movement between healthy controls and lean and obese knee osteoarthritis patients. Fifty-five subjects were included in this study, distributed over three groups: healthy controls (n=22), lean knee osteoarthritis (n=14), and obese knee OA patients (n=19). All subjects were instructed to perform sit-to-stand transfers at self-selected, comfortable speed. A three-dimensional movement analysis was performed to investigate compensatory mechanisms and knee and hip kinetics during sit-to-stand movement. No difference in sit-to-stand speed was found between lean knee OA patients and healthy controls. Obese knee osteoarthritis patients, however, have reduced hip and knee range of motion, which is associated with reduced peak hip and knee moments. Reduced vertical ground reaction force in terms of body weight and increased medial ground reaction forces indicates use of compensatory mechanisms to unload the affected knee in the obese knee osteoarthritis patients. We believe that an interplay between obesity and knee osteoarthritis leads to altered biomechanics during sit-to-stand movement, rather than knee osteoarthritis alone. From this perspective, obesity might be an important target to restore healthy sit-to-stand biomechanics in obese knee OA patients.

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