Machine learning is changing osteoporosis detection: an integrative review.

This study presents a comparative analysis of the classification performance of facial and emotion recognition systems using Machine Learning (ML) and Deep Learning (DL) algorithms. The primary objective of this work is to evaluate the applicability of emotion recognition in fields such as psychotherapy and crime analysis, using the FER-2013 dataset.The study was conducted with ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Decision Trees, and Gradient Boosting, as well as the DL algorithm, Convolutional Neural Network (CNN). Supported by the application of feature selection, preprocessing, and feature extraction techniques, the model’s performance was measured using standard metrics such as accuracy, precision, F1-score, and AUC-ROC.The experimental results showed that the highest classification accuracy (46.61%) was achieved with the CNN model. While the ML models generally offered lower accuracy, they provided advantages in terms of computational efficiency in specific scenarios.This study aims to contribute to the literature by providing a comparative analysis of ML and DL algorithms and by highlighting the effect of data preprocessing on performance. The findings set targets for future work, such as real-time system integration and hybrid model development.

The screening and diagnosis of osteoporosis in patients with type 2 diabetes mellitus (T2DM) based on bone mineral density remains challenging because of the limited availability and accessibility of dual-energy X-ray absorptiometry. We aimed to develop and validate models to predict the risk of osteoporosis in postmenopausal women with T2DM based on machine learning (ML) algorithms. This retrospective study included 303 postmenopausal women with T2DM. To develop prediction models for osteoporosis, we applied nine ML algorithms combined with demographic, clinical, and laboratory data. The least absolute shrinkage and selection operator were used to perform feature selection. We used the bootstrap resampling technique for model training and validation. To test the performance of the models, we calculated indices including the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve, and decision curve analysis. Furthermore, we conducted fivefold cross-validation for parameter optimization and model validation. Feature importance was assessed using the SHapley additive explanation (SHAP). We identified 10 independent predictors as the most valuable features. An AUROC of 0.616-1.000 was observed for nine ML algorithms. The extreme gradient boosting (XGBoost) model exhibited the best performance, outperforming conventional risk assessment tools and registering 0.993 in the training set, 0.798 in the validation set, and 0.786 in the test set for fivefold cross-validation. Using SHAP, we found that the explanatory variables contributed to the model and their relationship with osteoporosis occurrence. Furthermore, we developed a user-friendly tool for calculating the risk of osteoporosis. With the integration of demographic and clinical risk factors, ML algorithms can accurately predict osteoporosis. The XGBoost model showed ideal performance. With the incorporation of these models in the clinic, patients may benefit from early osteoporosis diagnosis and treatment.

Wind energy has the possibility for bringing out energy in a very constant and sustainable manner being a notable and eligible source. Although, wind energy does include numerous challenges like, the halted asset of wind plants, early investment costs, and therefore, the strain in discovering areas of wind efficiency. The major objective for proposing this work is to determine the power efficiency of wind turbines, which will also aid in the formulation of a proposal to reduce wind turbine maintenance costs. During this research, data analysis of turbine generators is performed on day to day wind speed info using machine learning and deep learning algorithms. A way is put forward by us to support deep learning and machine learning algorithms which can predict different values of power reliably. Hence, the execution of machine and deep learning algorithms are analyzed. For forecasting for a longer term these algorithms may be used for wind generation rate with historical relation to wind speed info. Index Terms: Wind turbine,machine learning algorithm

Background: Osteoporosis is a systemic skeletal disorder marked by reduced bone density and microarchitectural deterioration, leading to increased fracture risk. While the dual-energy X-ray absorptiometry (DEXA) scan is the World Health Organization (WHO)-recommended diagnostic standard, its limitations necessitate alternative methods. Emerging magnetic resonance imaging (MRI) techniques, radiomics, and machine learning promise to enhance osteoporosis diagnosis through detailed analysis of lumbar MRI apparent diffusion coefficient (ADC) maps, potentially revolutionizing early detection and treatment strategies. Objectives: In this study, we are going to evaluate the performance of machine learning (ML) models using radiomics features of lumbar MRI ADC map for osteoporosis detection, and to identify significant features and their diagnostic thresholds. Specific performance metrics such as accuracy, sensitivity, specificity, and Area Under the receiver operating characteristic (ROC) Curve (AUC) were assessed. Patients and Methods: This retrospective study employed a cross-sectional design, with a total of 140 cases, including 21 with osteoporosis. The study's inclusion criteria consisted of concurrent lumbar MRI and DEXA within a year, while exclusion criteria included infectious or neoplastic lumbar lesions, fractures, instrumentation, significant osteodegenerative changes, cases where the first four lumbar vertebrae were not included in the imaging field, and absence of diffusion-weighted imaging. Manual segmentation of lumbar vertebrae from ADC maps was performed to create a comprehensive dataset, comprising 5,580 radiomics features per case. Subsequently, the top five features selected by fast correlation-based filter (FCBF) were used to test the performance of seven Machine Learning algorithms (k-Nearest neighbors, decision tree, random forest, logistic regression, support vector machine, naive bayes, and neural network). Statistical tests and ROC curve analysis were conducted to determine the significance and thresholds of these features. Results: The study included 140 cases, with 132 females (94.3%) and 8 males (5.7%), and a mean age of 65.32 ± 8.50 years. The mean BMI was 31.43 ± 5.53 kg/m² for females and 26 ± 3.59 kg/m² for males. In terms of demographic differences, no significant age difference was found between the osteoporotic and non-osteoporotic groups (P = 0.889). However, the osteoporotic group had significantly lower mean body weight (64.90 ± 10.13 kg vs. 74.68 ± 13.94 kg, P = 0.003) and BMI (27.40 ± 4.38 kg/m² vs. 31.77 ± 5.52 kg/m², P = 0.001) compared to the non-osteoporotic group. The median interval between DEXA and lumbar MRI was 1 month (range 0.1 - 11.87 months). The Neural Network model demonstrated the highest performance with an AUC of 0.616 and a classification accuracy of 0.764 using all features. The Naive Bayes model, using the top five features selected by FCBF, showed the highest performance with an AUC of 0.913, accuracy of 0.907, sensitivity of 0.667, and specificity of 0.95. All ML models’ performance were elevated by feature selection. Independent t-tests and Mann-Whitney U tests identified 521 and 670 significant features, respectively (P < 0.05). ROC analysis revealed 58 features with AUC values above 0.70. Conclusion: This study's findings suggest that ML models, particularly the Naive Bayes algorithm, can effectively use lumbar ADC map radiomics to diagnose osteoporosis. These findings could enhance early detection and treatment strategies, potentially improving patient outcomes and reducing the burden of osteoporotic fractures. This study also established threshold values for significant features.

Acute inflammation of the meninges the protective membranes enveloping the brain and spinal cord is referred to as meningitis. NGAL, a protein classified as an acute-phase reactant. We aimed to assess serum NGAL levels in individuals with meningitis in this study and to assess potential associations with the evaluated biochemical markers. A case-control study was conducted involving 120 Iraqi participants (60 individuals diagnosed with meningitis, 60 individuals diagnosed as control). Serum levels of NGAL and various markers were measured in all subjects. Statistical evaluation showed increase in hs-CRP, TNF-α, IL-6, CTX and NGAL levels among individuals with meningitis as compared to control group, respectively (10±1 versus 3.5±0.3, P=0.03), (50±4 versus 13±2, P=0.01), (20±1 versus 8±0.5, P=0.03), (0.8±0.1 versus 0.45±0.1, P=0.01) and (165±20 versus 135±10, P=0.04). NGAL exhibited a strong direct positive correlation with hs-CRP, TNF-α, IL-6 and CTX levels. The present study demonstrated individuals with meningitis have significantly elevated serum levels of NGAL as compared to control group, and a strong direct correlation observed between NGAL and hs-CRP, TNF-α, IL-6 and CTX. These findings proposed that NGAL expression may serve as a new biochemical marker for the early detection and diagnosis of osteoporosis in patients with meningitis.

Osteoporosis is a progressive skeletal disorder characterized by decreased Bone Mineral Density (BMD) and structural deterioration, leading to an increased risk of fractures. Traditional diagnostic methods, such as Dual-Energy X-ray Absorptiometry (DXA) and the Fracture Risk Assessment Tool (FRAX), often fail to detect osteoporosis at an early stage due to their limited sensitivity and accessibility. Recent advancements in Artificial Intelligence (AI), Deep Learning (DL), and Machine Learning (ML) have shown significant potential in improving osteoporosis detection. However, existing models face challenges related to data privacy, explainability, computational complexity, and generalizability across diverse populations. This study introduces Federated Graph-based Multimodal Osteoporosis Diagnosis (FGMOD), a novel AI-driven framework that integrates Federated Learning (FL) and Graph Neural Networks (GNNs) for enhanced osteoporosis risk assessment. Unlike traditional DL models that rely on centralized training, FL ensures privacy-preserving learning across multiple hospitals and institutions. Simultaneously, GNNs model trabecular bone connectivity, offering deeper insights into microarchitectural changes in osteoporotic bones. Multimodal data fusion, incorporating structured clinical data, DXA scans, and biochemical markers, significantly improves prediction accuracy and interpretability. The proposed FGMOD model was validated on a real-world dataset comprising 10 000 patient records, 5000 DXA scans, and biochemical profiles. Comparative evaluation with state-of-the-art AI models demonstrated superior performance, achieving 94.2% accuracy, 93.4% precision, 93.1% recall, and a ROC-AUC score of 0.96. Unlike CNN-based models, which rely heavily on high-resolution images, FGMOD effectively extracts clinically relevant features from graph-based bone connectivity analysis, ensuring robust and interpretable predictions. Furthermore, the use of SHAP-based feature importance analysis provides clinicians with an explainable AI decision-making process, enhancing trust in the model’s recommendations. Performance comparisons against CNN, Gradient Boosting, Random Forest, and Transfer Learning models revealed that FGMOD outperformed all baselines, particularly in privacy-aware federated training, osteoporosis risk stratification, and real-time inference on edge AI devices. Additionally, FGMOD reduced inference latency to 45 ms per sample, making it suitable for real-time clinical deployment. FGMOD represents a significant advancement in AI-powered osteoporosis detection, addressing key challenges such as privacy, generalizability, and interpretability. Future research should focus on expanding FL collaborations across global hospitals, integrating wearable sensor data for continuous bone health monitoring, and enhancing GNN-based osteoporosis modeling for faster inference on mobile healthcare platforms. This study paves the way for clinically reliable, privacy-preserving, and scalable AI solutions in osteoporosis risk assessment and early detection.

Internet of Medical Things (IoMT) envisions the interconnection of healthcare devices and sensors to enable a new class of applications relying on medical data processing and storage. Moreover, machine learning and deep learning algorithms applied in healthcare domain allow health professionals to monitor, diagnose, focus and highlight the region of the problem and propose the required and accurate solution in the shortest duration possible. This paper surveys the state of art machine and deep learning algorithms in IoT healthcare.

The rapid growth of the Internet and information technologies and also the diminution of the price of hardware components like wireless sensors leads to the fast growth in Wireless Sensor Network (WSN). The WSN is a bunch/group of sensors that are located across the area, in order to monitor and determine environmental conditions such as temperature, humidity, vibrations, wind, water levels, pollution levels. WSNs are susceptible to major attacks like Denial of Service (DOS), Wormhole attack, Sinkhole attack, etc. The main threat to the WSN is because of the broadcast nature of the nodes in the network. Therefore, the security of WSNs is the essential part that must be done. Hence, to overcome these problems or threats, we are trying to detect it using AI technology. With the expanding fields of Machine Learning and Deep Learning, we can apply various algorithms in order to classify different types of attacks. Once we detect the attack properly, we can prevent it accordingly. We are using WSN-DS. It has 4 classes of attacks which are Grayhole, Blackhole, TDMA(Scheduling), and Flooding which comes under the category of DOS attacks. In this chapter, we have analysed and compared the accuracies of 5 main machine learning classification algorithms. Also, we have analysed the 1 deep learning algorithm. The ANN (Artificial Neural Network) and 5 machine learning algorithms have been trained on the dataset. Furthermore, we have used K-fold cross-validation to get even more accurate predictions. After analysis of these algorithms, we came to the point that, the Machine Learning algorithms like Random Forest, Support Vector Machines and Deep Learning algorithm, namely, Artificial Neural Network can help us for detecting intrusions in the system or network. This will help researchers in designing their own machine learning model on top of our suggested model.

Environmental exposure constantly changes with time and various interactions that can affect health outcomes. Machine learning (ML) or deep learning (DL) algorithms have been used to solve complex problems, such as multiple exposures and their interactions. This study developed predictive models for cause-specific mortality using ML and DL algorithms with the daily or hourly measured meteorological and air pollution data. The ML algorithm improved the performance compared to the conventional methods, even though the optimal algorithm depended on the adverse health outcomes. The best algorithms were extreme gradient boosting, ridge, and elastic net, respectively, for non-accidental, cardiovascular, and respiratory mortality with daily measurement; they were superior to the generalized additive model reducing a mean absolute error by 4.7%, 4.9%, and 16.8%, respectively. With hourly measurements, the ML model tended to outperform the conventional models, even though hourly data, instead of daily data, did not enhance the performance in some models. The proposed model allows a better understanding and development of robust predictive models for health outcomes using multiple environmental exposures.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-21768-9.

Feature mining for encrypted malicious traffic detection with deep learning and other machine learning algorithms

Big data is information management system through the integration of various traditional data techniques. Big data usually contains high volume of personal and authenticated information which makes privacy as a major concern. To provide security and effective processing of collected data various techniques are evolved. Machine Learning (ML) is considered as one of the data technology which handles one of the central and hidden parts of collected data. Same like ML algorithm Deep Learning (DL) algorithm learn program automatically from the data it is considered to enhance the performance and security of the collected massive data. This paper reviewed security issues in big data and evaluated the performance of ML and DL in a critical environment. At first, this paper reviewed about the ML and DL algorithm. Next, the study focuses towards issues and challenges of ML and their remedies. Following, the study continues to investigate DL concepts in big data. At last, the study figures out methods adopted in recent research trends and conclude with a future scope.

Aim: Machine learning (ML) and deep learning (DL) predictive models have been employed widely in clinical settings. Their potential support and aid to the clinician of providing an objective measure that can be shared among different centers enables the possibility of building more robust multicentric studies. This study aimed to propose a user-friendly and low-cost tool for COVID-19 mortality prediction using both an ML and a DL approach. Method: We enrolled 2348 patients from several hospitals in the Province of Reggio Emilia. Overall, 19 clinical features were provided by the Radiology Units of Azienda USL-IRCCS of Reggio Emilia, and 5892 radiomic features were extracted from each COVID-19 patient’s high-resolution computed tomography. We built and trained two classifiers to predict COVID-19 mortality: a machine learning algorithm, or support vector machine (SVM), and a deep learning model, or feedforward neural network (FNN). In order to evaluate the impact of the different feature sets on the final performance of the classifiers, we repeated the training session three times, first using only clinical features, then employing only radiomic features, and finally combining both information. Results: We obtained similar performances for both the machine learning and deep learning algorithms, with the best area under the receiver operating characteristic (ROC) curve, or AUC, obtained exploiting both clinical and radiomic information: 0.803 for the machine learning model and 0.864 for the deep learning model. Conclusions: Our work, performed on large and heterogeneous datasets (i.e., data from different CT scanners), confirms the results obtained in the recent literature. Such algorithms have the potential to be included in a clinical practice framework since they can not only be applied to COVID-19 mortality prediction but also to other classification problems such as diabetic prediction, asthma prediction, and cancer metastases prediction. Our study proves that the lesion’s inhomogeneity depicted by radiomic features combined with clinical information is relevant for COVID-19 mortality prediction.

Fetal health and early diagnosis refer to fetal development and regular contact with the mother’s uterus during pregnancy. Maximum pregnancy period complications put the fetus in a challenging situation that restricts proper growth and can result in deficiencies or death. Predicting risk factors before problems arise can help ensure a healthy pregnancy and promote appropriate fetal development. Forecasting the fetal health and growth state using a set of previously recognized patterns when creating a predictive classifier model with machine learning algorithms is essential. A fetus is an embryonic form of an unborn child that has not yet entered the world. Every three months of pregnancy are referred to as trimesters. The fetus grows and develops during this process, and regular examinations are crucial to ensure its health. There are many potential factors that could result in the newborn’s impairment or even death, which is a very serious situation that needs to be avoided. A Cardiotocography (C.T.G.), which is typically used to assess the heartbeat and uterine contractions, is one of the main techniques to assess the health of the fetus inside the womb. The doctor uses the data produced by C.T.G. test to assess the fetus state of his health and his words. However, there is space for error, making the doctors' data analysis untrustworthy. To assess the data and forecast fetal health, various machine learning and deep learning algorithms have been used. In this paper to demonstrate prediction accuracy using several categorization models and to evaluate the performance of each model.

Objectives: Language detection is the process of identifying a language associated with a text. The proposed system aims to detect the Dravidian language that is associated with the given text using different machine learning and deep learning algorithms. The paper presents an empirical analysis of the results obtained using the different models. It also aims to evaluate the performance of a language agnostic model for the purpose of language detection. Method: An empirical analysis of Dravidian language identification in social media text using machine learning and deep learning approaches with k-fold cross validation has been implemented. The identification of Dravidian languages, including Tamil, Malayalam, Tamil Code Mix, and Malayalam Code Mix, is performed using both machine learning (ML) and deep learning algorithms. The machine learning algorithms used for language detection are Naive Bayes (NB), Multinomial Logistic Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). The supervised Deep Learning (DL) models used include BERT, mBERT and language agnostic models. Findings: The language agnostic model outperform all other models considering the task of language detection in Dravidian languages. The results of both the ML and DL models are analyzed empirically with performance measures like accuracy, precision, recall, and f1-score. The accuracy associated with different machine learning algorithms varies from 85% to 89%. It is evident from the experimental result that the deep learning model outperformed with an accuracy of 98%. Novelty: The proposed system emphasizes on the use of the language agnostic model to implement the process of detecting Dravidian languages associated with the given text which provides a promising result of 98% accuracy which is higher than the existing methodologies. Keywords: Language, Machine learning, Deep learning, Transformer model, Encoder, Decoder

Purpose Osteoporosis is a common age-related disorder leading to increased bone fragility and risk of fracture. Early diagnosis of osteoporosis is a vital step in providing early therapeutic intervention. Serum cystatin C is a marker of early renal dysfunction, a predictor of cardiovascular and inflammatory diseases, and an inhibitor of the differentiation of osteoclast precursor cells. The purpose of this study was to evaluate the relationship between serum cystatin C and osteoporosis. Methods We enrolled 46 subjects who attended a health checkup and underwent measurement of bone status by quantitative ultrasound and determination of the level of serum cystatin C. A comparative study was conducted between those with and without osteoporosis for all subjects collectively and in two subgroups aged <65 and ≥65 years. Results Serum cystatin C levels were strongly correlated with age, creatinine, and bone status data, with significant negative correlations with stiffness, T-score, and percentage of young adult mean. Among patients with osteoporosis, serum cystatin C was significantly higher even after adjustment for age and sex, whereas no significant difference was noted in creatinine. For patients aged ≥ 65 years, serum cystatin C was significantly higher in subjects with osteoporosis, although there was no significant difference in age between normal subjects and those with osteoporosis. Conclusions To the best of our knowledge, this is the first study to demonstrate an association between serum cystatin C and osteoporosis. Serum cystatin C is significantly higher in osteoporosis and in particular may be a useful marker for osteoporosis among middle and elderly people aged ≥ 65 years. Measurement of serum cystatin C can be carried out easily and may contribute to early diagnosis and treatment of osteoporosis.