Statistical Machine Learning Models Research Articles

Time series decomposition of land surface temperature for long-term trend forecasting and impact on nesting sea turtle habitats in the Arabian Gulf

Abstract Improving Land Surface Temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as on important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats' long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, SEATS, and the Seasonal and Trend decomposition using Loess (STL) approach. The long-term trends in LST data are driven by climate change rather than seasonal fluctuations. We employed Neural Network Auto Regression (NNAR), BaggedETS, Exponential Smoothing models, and STL method to project future LST values. We also explored advanced forecasting models like Dynamic Harmonic Regression, TBATS, and SARIMA for comparative performance analysis. Extended warm periods were identified for Abu Ali Island between 2017 and 2018 through several decomposition methods, likely linked to the 2015-2016 El Niño event. We also conducted a Marine Heat Wave (MHW) analysis from 2010-2020, establishing a pronounced impact of the 2015-2016 El Niño on the Arabian Gulf. In nesting beach environments with high LST, marine heatwaves could have a significant impact on sea turtle populations without human intervention such as artificially cooling the nest temperature. SARIMA model showed higher forecasting precision for in-situ weather data while NNAR model demonstrated superior performance with remotely sensed data.

Foreign Exchange Rates Prediction Based on Comparative Models

With the development of globalization, fluctuations in the foreign exchange market have the huge potential to influence global economy. Machine Learning technology shows great potential in the field of financial forecasting than traditional statistical methods in recent years. This research selected statistical model Autoregressive Integrated Moving Average (ARIMA) and machine learning models Linear Regression and Long- and Short-Term Memory (LSTM) to make training and testing of historical data on the forex market. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-Squared were chosen as evaluation indexes to evaluate the performance of each model and compare their performance in forex exchange rate forecasts. The results show that ARIMA and LSTM could make better predictions than Linear Regression, between which LSTM has higher degree of fitting data. This research provides a detailed analysis comparing the performance of different models in forex exchange rate forecasting whose results are beneficial to guide forex market forecast in practical application and provide reference for future research.

New discrete fractional accumulation Grey Gompertz model for predicting carbon dioxide emissions

Predicting carbon dioxide emissions is crucial for addressing climate change and achieving environmental sustainability. Accurate emission forecasts provide policymakers with a basis for evaluating the effectiveness of policies, facilitating the design and implementation of emission reduction strategies, and helping businesses adjust their operations to adapt to market changes. Various methods, such as statistical models, machine learning, and grey prediction models, have been widely used in carbon dioxide emission prediction. However, existing research often lacks comparative analysis with other forecasting techniques. This paper constructs a new Discrete Fractional Accumulation Grey Gompertz Model (DFAGGM(1,1) based on grey system theory and provides a detailed solution process. The Whale Optimization Algorithm (WOA) is used to find the hyperparameters in the model. By comparing it with five benchmark models, the effectiveness of DFAGGM(1,1) in predicting carbon dioxide emissions data for China and the United States is validated.

Leveraging Hybrid Deep Learning Models for Enhanced Multivariate Time Series Forecasting

Time series forecasting is crucial in various domains, ranging from finance and economics to weather prediction and supply chain management. Traditional statistical methods and machine learning models have been widely used for this task. However, they often face limitations in capturing complex temporal dependencies and handling multivariate time series data. In recent years, deep learning models have emerged as a promising solution for overcoming these limitations. This paper investigates how deep learning, specifically hybrid models, can enhance time series forecasting and address the shortcomings of traditional approaches. This dual capability handles intricate variable interdependencies and non-stationarities in multivariate forecasting. Our results show that the hybrid models achieved lower error rates and higher R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} values, signifying their superior predictive performance and generalization capabilities. These architectures effectively extract spatial features and temporal dynamics in multivariate time series by combining convolutional and recurrent modules. This study evaluates deep learning models, specifically hybrid architectures, for multivariate time series forecasting. On two real-world datasets - Traffic Volume and Air Quality - the TCN-BiLSTM model achieved the best overall performance. For Traffic Volume, the TCN-BiLSTM model achieved an R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} score of 0.976, and for Air Quality, it reached an R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} score of 0.94. These results highlight the model’s effectiveness in leveraging the strengths of Temporal Convolutional Networks (TCNs) for capturing multi-scale temporal patterns and Bidirectional Long Short-Term Memory (BiLSTMs) for retaining contextual information, thereby enhancing the accuracy of time series forecasting.

Water quality level estimation using IoT sensors and probabilistic machine learning model

ABSTRACT Drinking water purity analysis is an essential framework that demands several real-world parameters to ensure the quality of water. So far, sensor-based analysis of water quality in specific environments is done concerning certain parameters including the PH level, hardness, TDS, etc. The outcome of such methods analyzes whether the environment provides potable water or not. Potable denotes the purified water that is free from all contaminations. This analysis gives an absolute solution whereas the demand for drinking water is a growing problem where the multiple-level estimations are essential to use the available water resources efficiently. In this article, we used a benchmark water quality assessment dataset for analysis. To perform a level assessment, we computed three major features namely correlation-entropy, dynamic scaling, and estimation levels, and annexed with the earlier feature vector. The assessment of the available data was performed using the statistical machine learning model that ensembles the random forest model and light gradient boost model (GBM). The probability of the ensemble model was done by the Kullback Libeler Divergence model. The proposed probabilistic model has achieved an accuracy of 96.8%, a sensitivity of 94.55%, and a specificity of 98.29%.

Spectral enhancement of PlanetScope using Sentinal-2 images to estimate soybean yield and seed composition

Soybean is an essential crop to fight global food insecurity and is of great economic importance around the world. Along with genetic improvements aimed at boosting yield, soybean seed composition also changed. Since conditions during crop growth and development influences nutrient accumulation in soybean seeds, remote sensing offers a unique opportunity to estimate seed traits from the standing crops. Capturing phenological developments that influence seed composition requires frequent satellite observations at higher spatial and spectral resolutions. This study introduces a novel spectral fusion technique called multiheaded kernel-based spectral fusion (MKSF) that combines the higher spatial resolution of PlanetScope (PS) and spectral bands from Sentinel 2 (S2) satellites. The study also focuses on using the additional spectral bands and different statistical machine learning models to estimate seed traits, e.g., protein, oil, sucrose, starch, ash, fiber, and yield. The MKSF was trained using PS and S2 image pairs from different growth stages and predicted the potential VNIR1 (705 nm), VNIR2 (740 nm), VNIR3 (783 nm), SWIR1 (1610 nm), and SWIR2 (2190 nm) bands from the PS images. Our results indicate that VNIR3 prediction performance was the highest followed by VNIR2, VNIR1, SWIR1, and SWIR2. Among the seed traits, sucrose yielded the highest predictive performance with RFR model. Finally, the feature importance analysis revealed the importance of MKSF-generated vegetation indices from fused images.

Statistical machine learning models for prediction of China's maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model.

Rescuing individuals at sea is a pressing global public health issue, garnering substantial attention from emergency medicine researchers with a focus on improving prevention and control strategies. This study aims to develop a Dynamic Bayesian Networks (DBN) model utilizing maritime emergency incident data and compare its forecasting accuracy to Auto-regressive Integrated Moving Average (ARIMA) and Seasonal Auto-regressive Integrated Moving Average (SARIMA) models. In this research, we analyzed the count of cases managed by five hospitals in Hainan Province from January 2016 to December 2020 in the context of maritime emergency care. We employed diverse approaches to construct and calibrate ARIMA, SARIMA, and DBN models. These models were subsequently utilized to forecast the number of emergency responders from January 2021 to December 2021. The study indicated that the ARIMA, SARIMA, and DBN models effectively modeled and forecasted Maritime Emergency Medical Service (EMS) patient data, accounting for seasonal variations. The predictive accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R 2) as performance metrics. In this study, the ARIMA, SARIMA, and DBN models reported RMSE of 5.75, 4.43, and 5.45; MAE of 4.13, 2.81, and 3.85; and R 2 values of 0.21, 0.54, and 0.44, respectively. MAE and RMSE assess the level of difference between the actual and predicted values. A smaller value indicates a more accurate model prediction. R 2 can compare the performance of models across different aspects, with a range of values from 0 to 1. A value closer to 1 signifies better model quality. As errors increase, R 2 moves further from the maximum value. The SARIMA model outperformed the others, demonstrating the lowest RMSE and MAE, alongside the highest R 2, during both modeling and forecasting. Analysis of predicted values and fitting plots reveals that, in most instances, SARIMA's predictions closely align with the actual number of rescues. Thus, SARIMA is superior in both fitting and forecasting, followed by the DBN model, with ARIMA showing the least accurate predictions. While the DBN model adeptly captures variable correlations, the SARIMA model excels in forecasting maritime emergency cases. By comparing these models, we glean valuable insights into maritime emergency trends, facilitating the development of effective prevention and control strategies.

FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation

AbstractEmulators, or reduced complexity climate models, are surrogate Earth system models (ESMs) that produce projections of key climate quantities with minimal computational resources. Using time‐series modeling or more advanced machine learning techniques, data‐driven emulators have emerged as a promising avenue of research, producing spatially resolved climate responses that are visually indistinguishable from state‐of‐the‐art ESMs. Yet, their lack of physical interpretability limits their wider adoption. In this work, we introduce FaIRGP, a data‐driven emulator that satisfies the physical temperature response equations of an energy balance model. The result is an emulator that (a) enjoys the flexibility of statistical machine learning models and can learn from data, and (b) has a robust physical grounding with interpretable parameters that can be used to make inference about the climate system. Further, our Bayesian approach allows a principled and mathematically tractable uncertainty quantification. Our model demonstrates skillful emulation of global mean surface temperature and spatial surface temperatures across realistic future scenarios. Its ability to learn from data allows it to outperform EBMs, while its robust physical foundation safeguards against the pitfalls of purely data‐driven models. We also illustrate how FaIRGP can be used to obtain estimates of top‐of‐atmosphere radiative forcing and discuss the benefits of its mathematical tractability for applications such as detection and attribution or precipitation emulation. We hope that this work will contribute to widening the adoption of data‐driven methods in climate emulation.

Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning

Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a potential diagnostic biomarker for PTSD is investigated in this study. We analyze an original cohort of 148 individuals exposed to the November 13, 2015, terrorist attacks in Paris. The interviews, conducted 5–11 months after the event, include individuals from similar socioeconomic backgrounds exposed to the same incident, responding to identical questions and using uniform PTSD measures. Using this dataset to collect nuanced insights that might be clinically relevant, we propose a three-step interdisciplinary methodology that integrates expertise from psychiatry, linguistics, and the Natural Language Processing (NLP) community to examine the relationship between language and PTSD. The first step assesses a clinical psychiatrist's ability to diagnose PTSD using interview transcription alone. The second step uses statistical analysis and machine learning models to create language features based on psycholinguistic hypotheses and evaluate their predictive strength. The third step is the application of a hypothesis-free deep learning approach to the classification of PTSD in our cohort. Results show that the clinical psychiatrist achieved a diagnosis of PTSD with an AUC of 0.72. This is comparable to a gold standard questionnaire (Area Under Curve (AUC) ≈ 0.80). The machine learning model achieved a diagnostic AUC of 0.69. The deep learning approach achieved an AUC of 0.64. An examination of model error informs our discussion. Importantly, the study controls for confounding factors, establishes associations between language and DSM-5 subsymptoms, and integrates automated methods with qualitative analysis. This study provides a direct and methodologically robust description of the relationship between PTSD and language. Our work lays the groundwork for advancing early and accurate diagnosis and using linguistic markers to assess the effectiveness of pharmacological treatments and psychotherapies.

GeoShapley: A Game Theory Approach to Measuring Spatial Effects in Machine Learning Models

This article introduces GeoShapley, a game theory approach to measuring spatial effects in machine learning models. GeoShapley extends the Nobel Prize–winning Shapley value framework in game theory by conceptualizing location as a player in a model prediction game, which enables the quantification of the importance of location and the synergies between location and other features in a model. GeoShapley is a model-agnostic approach and can be applied to statistical or black-box machine learning models in various structures. The interpretation of GeoShapley is directly linked with spatially varying coefficient models for explaining spatial effects and additive models for explaining non-spatial effects. Using simulated data, GeoShapley values are validated against known data-generating processes and are used for cross-comparison of seven statistical and machine learning models. An empirical example of house price modeling is used to illustrate GeoShapley’s utility and interpretation with real-world data. The method is available as an open source Python package named geoshapley.

An Enhanced Long-Term Wind Speed Prediction Using Dynamic Unified Ensemble Learning and Data Assimilation Techniques: A Case Study in Tamil Nadu, India

Reliable wind speed prediction is essential for effective grid management since wind energy is a key component of renewable energy generation. However, controlling wind speed just right is no easy feat since the wind flow changes constantly. This paper presents an innovative method for predicting wind speeds in the future. It uses state-of-the-art data assimilation methods and a dynamic unified ensemble learning model. For efficient wind energy planning and monitoring, WSP accuracy is crucial. Data from a single site limited the accuracy of WSPs in previous research. An improved accuracy of long-term wind speed predictions is achieved by the proposed model via the integration of data assimilation and ensemble learning. To increase forecast accuracy, the model uses sophisticated data assimilation methods like the Kalman filter to combine data from many sources. Specifically, the model employs the Stacked CNN + BiLSTM with Data Assimilator (SCBLSTM+DA) technique, which integrates Wind Speed (WS) data from adjacent areas with the CNN + BiLSTM-based Ensemble Learning Model (ELM) and the Four-Dimensional Variational and Ensemble Kalman Filter (4DVar/EnKF) Data Assimilation method. Using real-world wind speed data from nine meteorological stations in Tamil Nadu, India, we find that current prediction models, including both classical statistical and cutting-edge machine learning models, perform better. Further, unlike standalone models, the suggested model shows less susceptibility to changes in prediction time scales. Promising a solution to improve long-term wind speed predictions accuracy, this study has significant consequences for wind energy management and production.

Employing Graph Neural Networks for Predicting Electrode Average Voltages and Screening High-Voltage Sodium Cathode Materials.

For many years, humans have been relentlessly focused on enhancing battery longevity and boosting energy storage capacities. The performance and durability of a battery depend significantly on the material used for its electrodes. In this context, merging machine learning with density functional theory (DFT) calculations has emerged as a pivotal approach to advancing the exploration of battery crystal structures. We present a new method that combines a graph convolutional neural network (GNN) with a Transformer convolutional layer, which we call Transformer-GNN. To underscore its efficacy, we benchmarked Transformer-GNN against three established statistical machine learning models: Support Vector Machine, Random Forest, and XGBoost. We also developed a standard GNN, which we refer to as Basic-GNN. Additionally, we compared Basic-GNN with Transformer-GNN to highlight the improvements brought about by incorporating the Transformer convolutional layer. The Transformer-GNN model outperforms the other models, achieving the highest R2 value of 0.82 and the lowest mean squared error of 0.3161. Our findings demonstrate that the Transformer-GNN can profoundly understand battery crystal structures, thus forging the path toward more sophisticated and durable battery systems. Leveraging the GNN model's voltage predictions in tandem with the capacity data sourced from the database, we screened and pinpointed Na(NiO2)2 as a high-voltage (higher than 5 V), high-capacity sodium cathode material. We conducted DFT calculations on Na(NiO2)2 and revealed the migration mechanism of the Na ions.

Abstract PO5-07-01: A novel clinical risk score that can accurately predict the survival of young breast cancer patients: A UAE-based cohort study

Abstract Introduction: Breast cancer (BC) in women below the age of 40 accounts for approximately 7% of all BC cases. According to studies, BC in younger women is more likely to have negative tumor characteristics and outcomes compared to older women. The rate of early-onset breast cancer in the United Arab Emirates (UAE) is higher than in the Western population (24%, previously reported). Young breast cancer patients in the UAE have worse adverse features, including a higher grade, a larger tumor size, and lymph node involvement, as we reported previously. Therefore, combining several clinical indicators to predict recurrence risk in our unique, heterogeneous young breast cancer group is needed. Aim:The aim of this study is to develop a statistical machine learning model that can predict survival and the risk of recurrence in a young breast cancer population seen and treated in a large-volume tertiary cancer center in the United Arab Emirates (Tawam Hospital). Methods: Early-onset breast cancer patients from a retrospective observational cohort study were included in this study (N = 904). A broad range of clinical data was collected. A multivariate ridge Cox regression model was performed using age, histology, molecular type (ER,PR,HER-2 status), T-stage, N-Stage, M-stage, grade, and BMI, and the interaction term age*BMI at the time of diagnosis. The primary outcome was time-to-death. A novel breast cancer risk score (BCRS) was developed as the linear predictor of the fitted-ridge Cox regression model. Breast cancer patients were categorized into low, medium, and high risk using the 33th and 66th centiles of the novel BCRS. A univariate Cox regression model was performed with a categorized BCRS as a predictor of time-to-death. Hazard ratios (HR) with a 95% confidence interval (95% CI) were estimated. Kaplan-Meier (KM) curves were plotted and compared using the log-rank test. Time-dependent sensitivity and specificity were computed at five- and ten-year follow-ups, respectively. Statistical analyses were performed in R version 4.2.3. Results: The median age was 36 years (IQR: 32–38), and 64% were Arabic. Fifteen percent had an event with a median duration of follow-up of 15.4 years (95% CI: 13.6–17.1). The estimated HR of the categorized novel BCRS in a univariate Cox regression model was 1.968 (95% CI: 1.075- 3.601) and 8.040 (95% CI: 4.695–13.769) for the intermediate and high-risk groups as compared to the low-risk group, respectively. KM curves show a clear separation in terms of survival between the three risk groups, p< 0.0001 (Figure 1). A cutoff value of -1.733 gives a sensitivity of 93.8 and 90.6 with corresponding specificities of 31.42 and 31.31 at five- and ten-year follow-up, respectively. Conclusion: This is the first study in the region that uses clinical data that combines several clinical characteristics and aims to design a statistical machine learning model to predict the risk of death from breast cancer. Our novel model can be used in daily clinical practice to identify high risk breast cancer patients and aid clinical decision-making in patients with breast cancer. However, further validation studies are needed. Figure 1:KM curves of breast-cancer survival for patients with low-risk (BCRS in [-2.32, -1.63], black curve), intermediate-risk (BCRS in [-1.64, -1.25], red curve), and high-risk BCRS in [-1.26, 1.41], green curve). Citation Format: Aydah Al-Awadhi, Mohammed Hourani, Mawada Hussein, Fatima Alkindi, Lina Wahba, Abla AlAgha, Alaa Shoqeir, Mouza AlShebli, Atlal Abusanad, Amar Ahmad. A novel clinical risk score that can accurately predict the survival of young breast cancer patients: A UAE-based cohort study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-01.

Comparison of methods to aggregate climate data to predict crop yield: an application to soybean

High-dimensional climate data collected on a daily, monthly, or seasonal time step are now commonly used to predict crop yields worldwide with standard statistical models or machine learning models. Since the use of all available individual climate variables generally leads to calculation problems, over-fitting, and over-parameterization, it is necessary to aggregate the climate data used as predictors. However, there is no consensus on the best way to perform this task, and little is known about the impacts of the type of aggregation method used and of the temporal resolution of weather data on model performances. Based on historical data from 1981 to 2016 of soybean yield and climate on 3447 sites worldwide, this study compares different temporal resolutions (daily, monthly, or seasonal) and dimension reduction techniques (principal component analysis (PCA), partial least square regression, and their functional counterparts) to aggregate climate data used as inputs of machine learning and linear regression (LR) models predicting yields. Results showed that random forest models outperformed and were less sensitive to climate aggregation methods than LRs when predicting soybean yields. With our models, the use of daily climate data did not improve predictive performance compared to monthly data. Models based on PCA or averages of monthly data showed better predictive performance compared to those relying on more sophisticated dimension reduction techniques. By highlighting the high sensitivity of projected impact of climate on crop yields to the temporal resolution and aggregation of climate input data, this study reveals that model performances can be improved by choosing the most appropriate time resolution and aggregation techniques. Practical recommendations are formulated in this article based on our results.

Statistical parameterized physics-based machine learning digital shadow models for laser powder bed fusion process

This paper presents a statistical physics-based machine learning model for predicting defects, such as surface roughness and lack-of-fusion porosity, in the laser powder bed fusion of metals (PBF-LB/M) additive manufacturing process. The statistical physics-based model is calibrated and validated against controlled single-track experiments and used for statistical prediction for multi-layer and multi-track cases for PBF-LB/M defects. A mechanistic reduced-order-based stochastic calibration process is introduced to capture the stochastic nature of the melt pool. The calibrated physics-based digital shadow model is demonstrated for predicting the surface roughness of the National Institute of Standards and Technology (NIST) overhang part X4, with a difference of 9.3% compared to the experimental results. By leveraging data obtained from both the physics-based model and experiments, a machine learning model has been trained for fast predictions (inference time of 0.4 ms) with high accuracy (error bound of 6.7%). This model can predict melt pool geometries under various processing conditions, offering a control strategy for the PBF-LB/M process. Further, the trained machine learning model is showcased to demonstrate a control application of melt pool geometries (width and depth) for specific processing parameters. These developed models (physics-based and machine learning) serve as a digital shadow of the PBF-LB/M process, offering predictive capabilities to build a digital twin model for process control, optimization, and online monitoring.

Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia.

The inherent genetic heterogeneity of acute myeloid leukemia (AML) has challenged the development of precise and effective therapies. The objective of this study was to elucidate the genomic basis of drug resistance or sensitivity, identify signatures for drug response prediction, and provide resources to the research community. We performed targeted sequencing, high-throughput drug screening, and single-cell genomic profiling on leukemia cell samples derived from patients with AML. Statistical approaches and machine learning models were applied to identify signatures for drug response prediction. We also integrated large public datasets to understand the co-occurring mutation patterns and further investigated the mutation profiles in the single cells. The features revealed in the co-occurring or mutual exclusivity pattern were further subjected to machine learning models. We detected genetic signatures associated with sensitivity or resistance to specific agents, and identified five co-occurring mutation groups. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct subclones within patients with AML. Using the mutation pattern for drug response prediction demonstrates high accuracy in predicting sensitivity to some drug classes, such as MEK inhibitors for RAS-mutated leukemia. Our study highlights the importance of considering the gene mutation patterns for the prediction of drug response in AML. It provides a framework for categorizing patients with AML by mutations that enable drug sensitivity prediction.

Exploration and demonstration of explainable machine learning models in prosthetic rehabilitation-based gait analysis.

Quantitative gait analysis is important for understanding the non-typical walking patterns associated with mobility impairments. Conventional linear statistical methods and machine learning (ML) models are commonly used to assess gait performance and related changes in the gait parameters. Nonetheless, explainable machine learning provides an alternative technique for distinguishing the significant and influential gait changes stemming from a given intervention. The goal of this work was to demonstrate the use of explainable ML models in gait analysis for prosthetic rehabilitation in both population- and sample-based interpretability analyses. Models were developed to classify amputee gait with two types of prosthetic knee joints. Sagittal plane gait patterns of 21 individuals with unilateral transfemoral amputations were video-recorded and 19 spatiotemporal and kinematic gait parameters were extracted and included in the models. Four ML models-logistic regression, support vector machine, random forest, and LightGBM-were assessed and tested for accuracy and precision. The Shapley Additive exPlanations (SHAP) framework was applied to examine global and local interpretability. Random Forest yielded the highest classification accuracy (98.3%). The SHAP framework quantified the level of influence of each gait parameter in the models where knee flexion-related parameters were found the most influential factors in yielding the outcomes of the models. The sample-based explainable ML provided additional insights over the population-based analyses, including an understanding of the effect of the knee type on the walking style of a specific sample, and whether or not it agreed with global interpretations. It was concluded that explainable ML models can be powerful tools for the assessment of gait-related clinical interventions, revealing important parameters that may be overlooked using conventional statistical methods.

Machine learning models for classifying non-specific neck pain using craniocervical posture and movement

ObjectivePhysical therapists and clinicians commonly confirm craniocervical posture (CCP), cervical retraction, and craniocervical flexion as screening tests because they contribute to non-specific neck pain (NSNP). We compared the predictive performance of statistical machine learning (ML) models for classifying individuals with and without NSNP using datasets containing CCP and cervical kinematics during pro- and retraction (CKdPR). DesignExploratory, cross-sectional design. Setting and participantsIn total, 773 public service office workers (PSOWs) were screened for eligibility (NSNP, 441; without NSNP, 332). MethodsWe set up five datasets (CCP, cervical kinematics during the protraction, cervical kinematics during the retraction, CKdPR and combination of the CCP and CKdPR). Four ML algorithms–random forest, logistic regression, Extreme Gradient boosting, and support vector machine–were trained. Main outcome measuresModel performance were assessed using area under the curve (AUC), accuracy, precision, recall and F1-score. To interpret the predictions, we used Feature permutation importance and SHapley Additive explanation values. ResultsThe random forest model in the CKdPR dataset classified PSOWs with and without NSNP and achieved the best AUC among the five datasets using the test data (AUC, 0.892 [good]; F1, 0.832). The random forest model in the CCP dataset had the worst AUC among the five datasets using the test data [AUC, 0.738 (fair); F1, 0.715]. ConclusionML performance was higher for the CKdPR dataset than for the CCP dataset, suggesting that ML algorithms are more suitable than classical statistical methods for developing robust models for classifying PSOWs with and without NSNP.

A Systematic Review on Digital Soil Mapping Approaches in Lowland Areas

Digital soil mapping (DSM) around the world is mostly conducted in areas with a certain relief characterized by significant heterogeneities in soil-forming factors. However, lowland areas (e.g., plains, low-relief areas), prevalently used for agricultural purposes, might also show a certain variability in soil characteristics. To assess the spatial distribution of soil properties and classes, accurate soil datasets are a prerequisite to facilitate the effective management of agricultural areas. This systematic review explores the DSM approaches in lowland areas by compiling and analysing published articles from 2008 to mid-2023. A total of 67 relevant articles were identified from Web of Science and Scopus. The study reveals a rising trend in publications, particularly in recent years, indicative of the growing recognition of DSM’s pivotal role in comprehending soil properties in lowland ecosystems. Noteworthy knowledge gaps are identified, emphasizing the need for nuanced exploration of specific environmental variables influencing soil heterogeneity. This review underscores the dominance of agricultural cropland as a focus, reflecting the intricate relationship between soil attributes and agricultural productivity in lowlands. Vegetation-related covariates, relief-related factors, and statistical machine learning models, with random forest at the forefront, emerge prominently. The study concludes by outlining future research directions, highlighting the urgency of understanding the intricacies of lowland soil mapping for improved land management, heightened agricultural productivity, and effective environmental conservation strategies.

Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion.

Skeletal Class III malocclusion is one type of dentofacial deformity that significantly affects patients' facial aesthetics and oral health. The orthodontic treatment of skeletal Class III malocclusion presents challenges due to uncertainties surrounding mandibular growth patterns and treatment outcomes. In recent years, disease-specific radiographic features have garnered interest from researchers in various fields including orthodontics, for their exceptional performance in enhancing diagnostic precision and treatment effect predictability. The aim of this narrative review is to provide an overview of the valuable radiographic features in the diagnosis and management of skeletal Class III malocclusion. Based on the existing literature, a series of analyses on lateral cephalograms have been concluded to identify the significant variables related to facial type classification, growth prediction, and decision-making for tooth extractions and orthognathic surgery in patients with skeletal Class III malocclusion. Furthermore, we summarize the parameters regarding the inter-maxillary relationship, as well as different anatomical structures including the maxilla, mandible, craniofacial base, and soft tissues from conventional and machine learning statistical models. Several distinct radiographic features for Class III malocclusion have also been preliminarily observed using cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI).