Classification and prediction of Orthopedic disease based on lumber and pelvic state of patients

Rubaiyat, Nadia; Apsara, Anika Islam; Farabe, Abdullah Al; Ishtiak, Ifaz

doi:10.1109/icecct.2019.8869540

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…The CAD system extracts features using a pretrained ResNet-101 model and classifies them using a support vector machine (SVM) classifier. Ultrasound images are frequently influenced by speckle noise, which reduces data quality and CAD system efficiency [ 16 ]. To identify bone fractures in human fingers via image analysis, we developed and tested an approach.…”

Section: Related Workmentioning

confidence: 99%

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

Xiu

Rong

Zhang

2022

Computational and Mathematical Methods in Medicine

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The area of medical diagnosis has been transformed by computer-aided diagnosis (CAD). With the advancement of technology and the widespread availability of medical data, CAD has gotten a lot of attention, and numerous methods for predicting different pathological diseases have been created. Ultrasound (US) is the safest clinical imaging method; therefore, it is widely utilized in medical and healthcare settings with computer-aided systems. However, owing to patient movement and equipment constraints, certain artefacts make identification of these US pictures challenging. To enhance the quality of pictures for classification and segmentation, certain preprocessing techniques are required. Hence, we proposed a three-stage image segmentation method using U-Net and Iterative Random Forest Classifier (IRFC) to detect orthopedic diseases in ultrasound images efficiently. Initially, the input dataset is preprocessed using Enhanced Wiener Filter for image denoising and image enhancement. Then, the proposed segmentation method is applied. Feature extraction is performed by transform-based analysis. Finally, obtained features are reduced to optimal subset using Principal Component Analysis (PCA). The classification is done using the proposed Iterative Random Forest Classifier. The proposed method is compared with the conventional performance measures like accuracy, specificity, sensitivity, and dice score. The proposed method is proved to be efficient for detecting orthopedic diseases in ultrasound images than the conventional methods.

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Section: Related Workmentioning

confidence: 99%

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

Xiu

Rong

Zhang

2022

Computational and Mathematical Methods in Medicine

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“…They tried ensemble methods such as AdaBoost and voting ensemble methods to improve the accuracy of poorly performing algorithms. Rubaiyat et al built a disease prediction model based on three machine learning algorithms: logistic regression [24], random forest classifier, and KNN, and used a dataset of 310 orthopedic disease patients for training. The experimental results show that the prediction accuracy rate of the prediction model based on the random forest algorithm reaches 89%.…”

Section: Related Workmentioning

confidence: 99%

“…Rubaiyat [24] Orthopedic Disease √ Logistic Regression, Random Forest Classifier, KNN Bhoyar [30] Cardiovascular Diseases √ Neural Networks…”

Section: Integrated Disease Risk Prediction Algorithmmentioning

confidence: 99%

Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes

Zhou

et al. 2023

Electronics

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With the innovation of technologies such as sensors and artificial intelligence, some nursing homes use wearable devices to monitor the movement and physiological indicators of the elderly and provide prompts for any health risks. Nevertheless, this kind of risk warning is a decision based on a particular physiological indicator. Therefore, such decisions cannot effectively predict health risks. To achieve this goal, we propose a model Lidom (A LightGBM-based Disease Prediction Model) based on the combination of the LightGBM algorithm, InterpretML framework, and sequence confrontation network (SeqGAN). The Lidom model first solves the problem of uneven samples based on the sequence confrontation network (SeqGAN), then trains the model based on the LightGBM algorithm, uses the InterpretML framework for analysis, and finally obtains the best model. This paper uses the public dataset MIMIC-III, subject data, and the early diabetes risk prediction dataset in UCI as sample data. The experimental results show that the Lidom model has an accuracy rate of 93.46% for disease risk prediction and an accuracy rate of 99.8% for early diabetes risk prediction. The results show that the Lidom model can provide adequate support for the prediction of the health risks of the elderly.

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Orthopedic disease classification based on breadth-first search algorithm

Elshewey,

Osman

2024

Sci Rep

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Orthopedic diseases are widespread worldwide, impacting the body’s musculoskeletal system, particularly those involving bones or hips. They have the potential to cause discomfort and impair functionality. This paper aims to address the lack of supplementary diagnostics in orthopedics and improve the method of diagnosing orthopedic diseases. The study uses binary breadth-first search (BBFS), binary particle swarm optimization (BPSO), binary grey wolf optimizer (BGWO), and binary whale optimization algorithm (BWAO) for feature selections, and the BBFS makes an average error of 47.29% less than others. Then we apply six machine learning models, i.e., RF, SGD, NBC, DC, QDA, and ET. The dataset used contains 310 instances and six distinct features. Through experimentation, the RF model led to optimal outcomes during comparison to the remaining models, with an accuracy of 91.4%. The parameters of the RF model were optimized using four optimization algorithms: BFS, PSO, WAO, and GWO. To check how well the optimized RF works on the dataset, this paper uses prediction evaluation metrics such as accuracy, sensitivity, specificity, F-score, and the AUC curve. The results showed that the BFS-RF can improve the performance of the original classifier compared with others with 99.41% accuracy.

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Classification and prediction of Orthopedic disease based on lumber and pelvic state of patients

Cited by 4 publications

References 9 publications

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes

Orthopedic disease classification based on breadth-first search algorithm

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