Estimation of lower limb joint moments based on the inverse dynamics approach: a comparison of machine learning algorithms for rapid estimation

Mansour, Mohammed; Serbest, Kasım; Kutlu, Mustafa; Çilli, Murat

doi:10.1007/s11517-023-02890-3

Cited by 5 publications

(3 citation statements)

References 37 publications

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“…DT is a decision-making method that has a tree structure and Random Forest (RF) is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. It improves predictive accuracy with average and reduces over fitting (Gnanapriya et al, 2010 ; Schonlau and Zou, 2020 ; Mansour et al, 2023 ). In this study, the DT were constructed with a maximum depth of 1 for estimating the three joints moment.…”

Section: Methodsmentioning

confidence: 99%

“…The performance of ML algorithms is critical to their usefulness and effectiveness in solving real-world problems (Hossin and Sulaiman, 2015 ; Santafe et al, 2015 ; Mansour et al, 2023 ). High-performing algorithms can make more accurate predictions, process data more efficiently, scale to handle large datasets, and be more interpretable, leading to better decision-making and improved outcomes.…”

Section: Methodsmentioning

confidence: 99%

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Anemia detection through non-invasive analysis of lip mucosa images

Mahmud,

Donmez,

Mansour

et al. 2023

Front. Big Data

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This paper aims to detect anemia using images of the lip mucosa, where the skin tissue is thin, and to confirm the feasibility of detecting anemia noninvasively and in the home environment using machine learning (ML). Data were collected from 138 patients, including 100 women and 38 men. Six ML algorithms: artificial neural network (ANN), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM) which are widely used in medical applications, were used to classify the collected data. Two different data types were obtained from participants' images (RGB red color values and HSV saturation values) as features, with age, sex, and hemoglobin levels utilized to perform classification. The ML algorithm was used to analyze and classify images of the lip mucosa quickly and accurately, potentially increasing the efficiency of anemia screening programs. The accuracy, precision, recall, and F-measure were evaluated to assess how well ML models performed in predicting anemia. The results showed that NB reported the highest accuracy (96%) among the other ML models used. DT, KNN and ANN reported an accuracies of (93%), while LR and SVM had an accuracy of (79%) and (75%) receptively. This research suggests that employing ML approaches to identify anemia will help classify the diagnosis, which will then help to create efficient preventive measures. Compared to blood tests, this noninvasive procedure is more practical and accessible to patients. Furthermore, ML algorithms may be created and trained to assess lip mucosa photos at a minimal cost, making it an affordable screening method in regions with a shortage of healthcare resources.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Anemia detection through non-invasive analysis of lip mucosa images

Mahmud,

Donmez,

Mansour

et al. 2023

Front. Big Data

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“…Deep learning (DL) and deep transfer learning are important elements of data science, with applications including statistics and predictive modeling (Iman et al, 2023;Kumar et al, 2023;Mansour et al, 2023b;Sharifani and Amini, 2023). Convolutional neural networks (CNN) are specific architectures for input formats, such as images, and are typically used for image recognition and classification, as shown in Figure 2 (Li et al, 2016;Aloysius and Geetha, 2017;Bharadiya, 2023;Mansour et al, 2023a).…”

Section: Introductionmentioning

confidence: 99%

Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks

Mahmud,

Mansour,

Donmez

et al. 2023

Front. Big Data

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Anemia is defined as a drop in the number of erythrocytes or hemoglobin concentration below normal levels in healthy people. The increase in paleness of the skin might vary based on the color of the skin, although there is currently no quantifiable measurement. The pallor of the skin is best visible in locations where the cuticle is thin, such as the interior of the mouth, lips, or conjunctiva. This work focuses on anemia-related pallors and their relationship to blood count values and artificial intelligence. In this study, a deep learning approach using transfer learning and Convolutional Neural Networks (CNN) was implemented in which VGG16, Xception, MobileNet, and ResNet50 architectures, were pre-trained to predict anemia using lip mucous images. A total of 138 volunteers (100 women and 38 men) participated in the work to develop the dataset that contains two image classes: healthy and anemic. Image processing was first performed on a single frame with only the mouth area visible, data argumentation was preformed, and then CNN models were applied to classify the dataset lip images. Statistical metrics were employed to discriminate the performance of the models in terms of Accuracy, Precision, Recal, and F1 Score. Among the CNN algorithms used, Xception was found to categorize the lip images with 99.28% accuracy, providing the best results. The other CNN architectures had accuracies of 96.38% for MobileNet, 95.65% for ResNet %, and 92.39% for VGG16. Our findings show that anemia may be diagnosed using deep learning approaches from a single lip image. This data set will be enhanced in the future to allow for real-time classification.

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