Knee osteoarthritis severity classification with ordinal regression module

Yong, Ching Wai; Teo, Kareen; Murphy, Belinda; Hum, Yan Chai; Tee, Yee Kai; Xia, Kaijian; Lai, Khin Wee

doi:10.1007/s11042-021-10557-0

Cited by 35 publications

(32 citation statements)

References 29 publications

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“…The k-Means clustering technique with 11 specified features from various categories of medical equipment effectively divided the 13,352 units into three priority levels. Similar to the results produced in preventive and corrective maintenance prioritisation systems, the k-Means clustering technique with the support of 11 features from various medical equipment categories can create a useful assessment system for the replacement programme prioritisation by selecting the distance metric of Squared Euclidean and five ( 5 ) replicate number during the cluster analysis process. Moreover, the results show that the proposed clustering technique measures the dataset of medical equipment without prioritising to the function or type of equipment.…”

Section: Resultsmentioning

confidence: 94%

“…Hence, the clustering technique developed a practical preventive maintenance prioritisation system for medical equipment by applying the Squared Euclidean distance in k-Means. The cluster analysis in the first stage was executed several times by selecting the Squared Euclidean for the distance metric and the replicating number was proposed to be set as five ( 5 ) to get the best centroid points. These centroid points are essential to split the medical equipment samples to the appropriate priority level region based on the specified number of clusters and generate better clustering segregation of medical equipment priority levels.…”

Section: Resultsmentioning

confidence: 99%

“…Subsequently, the outputs were fed into the second stage, which is model training. Six different machine learning algorithms were present in this stage: ( 1 ) Decision Tree, ( 2 ) Linear Discriminant, ( 3 ) Naïve Bayes, ( 4 ) Support Vector Machine, ( 5 ) K-Nearest Neighbour, and ( 6 ) Bagged Trees were tested by measuring the specified features of various types of medical equipment. The reason of selecting these six classifiers were because of its suitability with the characteristics of medical equipment dataset involved in this study.…”

Section: Methodsmentioning

confidence: 99%

“…Procedures involved in healthcare services varying from diagnosis, therapeutics, rehabilitation, screening, prevention, and monitoring depend on medical equipment efficiency ( 4 ). The specialised equipment extensively assists healthcare practitioners during the early phase of symptom detection to curb health deterioration ( 5 ). Healthcare services delivery is almost impossible without proper maintenance of medical equipment ( 6 ).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

Zamzam

Al-Ani

Wahab

et al. 2021

Front. Public Health

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The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations' expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment's preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment's preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce.

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Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

Zamzam

Al-Ani

Wahab

et al. 2021

Front. Public Health

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recent studies have solved similar problems with ordinal regressors based on deep neural networks and other machine-learning algorithms, e.g., image ordinal estimation [21], knee osteoarthritis severity [22], degree of building damage [23], and Twitter sentimental analysis [24]. These problems present a class attribute with an ordinal domain, such as the dwell time of import containers in a yard.…”

Section: Introductionmentioning

confidence: 99%

Dwell Time Estimation of Import Containers as an Ordinal Regression Problem

et al. 2021

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The optimal stacking of import containers in a terminal reduces the reshuffles during the unloading operations. Knowing the departure date of each container is critical for optimal stacking. However, such a date is rarely known because it depends on various attributes. Therefore, some authors have proposed estimation algorithms using supervised classification. Although supervised classifiers can estimate this dwell time, the variable “dwell time” takes ordered values for this problem, suggesting using ordinal regression algorithms. Thus, we have compared an ordinal regression algorithm (selected from 15) against two supervised classifiers (selected from 30). We have set up two datasets with data collected in a container terminal. We have extracted and evaluated 35 attributes related to the dwell time. Additionally, we have run 21 experiments to evaluate both approaches regarding the mean absolute error modified and the reshuffles. As a result, we have found that the ordinal regression algorithm outperforms the supervised classifiers, reaching the lowest mean absolute error modified in 15 (71%) and the lowest reshuffles in 14 (67%) experiments.

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Automated knee osteoarthritis severity classification using three‐stage preprocessing method andVGG16architecture

Saini

Khosla

Chand

et al. 2023

Int J Imaging Syst Tech

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As a nutshell, an early diagnosis of Knee Osteoarthritis (KOA) enhances the likelihood of an individual receiving treatment at its onset and prevents joint replacement surgery. Computer-Aided-Diagnosis (CAD) from the radiograph images have gained a wide-spread attention in automated grading of KOA severity. But radiograph images have certain limitations, such as presence of too much noise and uneven contrast distribution across the image that impacts the accuracy and reliability of CAD systems. Therefore, a novel threestage pre-processing method has been proposed using a combination of different techniques applied at sequential stages such as noise-reduction using gaussian-filter, normalization using pixel-centering method, and balanced contrast enhancement technique. A transfer-learning based VGG16 architecture has been used for severity classification. Our classification framework outperforms the existing state-of-the-art methods achieving an excellent accuracy of 89.95\%. Therefore, our system can be used by the radiologists and physicians as a decision-support tool in their daily diagnosis procedure.

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Knee osteoarthritis severity classification with ordinal regression module

Cited by 35 publications

References 29 publications

Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

Dwell Time Estimation of Import Containers as an Ordinal Regression Problem

Automated knee osteoarthritis severity classification using three‐stage preprocessing method andVGG16architecture

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