Machine Learning Regression Algorithms Research Articles

Hybrid machine learning for stock price prediction in the Moroccan banking sector

Analyzing historical stock market data using machine-learning techniques is crucial for data scientists and researchers to optimize stock price prediction models. This study uses machine learning regression algorithms and feature selection methods to optimize a simulated stock price prediction model using real historical data from Bank of Africa, a Moroccan bank. The approach compares multiple supervised regression algorithms, such as linear regression, extreme gradient boosting, ordinary least squared, random forest regressor, a linear least-squares L2-regularized, epsilon-support vector regression, and linear support vector regression. Each of these algorithms is associated with different feature selection algorithms to improve the performance of the prediction model. The analysis results revealed that hybridizing algorithms between the highest score percentiles, univariate linear regression, and linear support vector regression perform better according to the root mean squared error and R2-Score measures. This approach overcomes the problems associated with high-dimensional data by reducing the number of features and improving prediction accuracy.

A machine learning framework for the prediction of antibacterial capacity of silver nanoparticles

Abstract The biocompatibility property has made silver nanoparticles powerful candidates for various nanomedical applications. Research interest in silver nanoparticles as a viable alternative to antibiotics is gaining more attention due to their enhanced antimicrobial activity, better antibacterial activity and low cytotoxicity. Machine Learning (ML) has become a state-of-the-art analytic and modelling tool in recent times, due to its prediction capabilities and increased accuracy of the results. In this work, we present machine-learning techniques to predict the antibacterial capacity of silver nanoparticles and extended the work on antifungal studies. In the first phase, we reviewed 50 articles and collected data points for training the model, which consists of features such as core size, shape of the nanoparticle, dosage, bacteria/fungi species and zone of inhibition (ZOI). Then, we trained the data using eight different machine-learning regression algorithms and validated the models' performance using four metrics such as RMSE, MSE, MAE and R2. Furthermore, the importance of features used in the prediction models has been evaluated. The feature importance revealed that the core size of silver nanoparticles is the prominent feature in the prediction of the antibacterial capacity. The optimum model for the prediction of antibacterial and antifungal activity has been identified. Finally, the model’s validation has also been demonstrated. This work enables researchers to utilize Machine Learning which in turn can address the challenges of time consumption, and cost in laboratory experiments while minimising the reliance on trial and error.

Data-driven, explainable machine learning model for predicting volatile organic compounds’ standard vaporization enthalpy

The accurate prediction of standard vaporization enthalpy (ΔvapHm°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental methods for predicting ΔvapHm° of VOCs, machine learning (ML) models enable a high-throughput, cost-effective property estimation. But despite a rising momentum, existing ML algorithms still present limitations in prediction accuracy and broad chemical applications.In this work, we present a data driven, explainable supervised ML model to predict ΔvapHm° of VOCs. The model was built on an established experimental database of 2410 unique molecules and 223 VOCs categorized by chemical groups. Using supervised ML regression algorithms, the Random Forest successfully predicted VOCs' ΔvapHm° with a mean absolute error of 3.02 kJ mol−1 and a 95% test score. The model was successfully validated through the prediction of ΔvapHm° for a known database of VOCs and through molecular group hold-out tests. Through chemical feature importance analysis, this explainable model revealed that VOC polarizability, connectivity indexes and electrotopological state are key for the model's prediction accuracy. We thus present a replicable and explainable model, which can be further expanded towards the prediction of other thermodynamic properties of VOCs.

Application of machine learning in predicting the thermal conductivity of single-filler polymer composites

Polymer composites with superior thermal conductivity and low electrical conductivity are pivotal in the cooling of electronic devices. Despite their prevalence, the accurate prediction of the thermal conductivity of these composites remains a challenge. The emergence of machine learning (ML) provides a groundbreaking approach to solving this issue. In this study, we constructed a comprehensive dataset collected from previous experimental papers and successfully predicted the thermal conductivity of single-filler polymer composites using four ML regression algorithms: random forest regression (RFR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and Gaussian process regression (GPR). By employing feature engineering to select pertinent features from the original datasets, the accuracy of the four models on the test set was improved, among which GBDT exhibited the highest accuracy with the Pearson correlation coefficient value of 0.981. Factors such as filler volume fraction and matrix thermal conductivity significantly influence the thermal conductivity of composite materials, while the thermal conductivity of the fillers has a relatively minor impact. Additionally, we identified the topological polar surface area (TPSA) as a crucial descriptor for surface modifications, quantifying diverse surface-modifying agents. Due to the incorporation of more descriptors, ML models exhibit higher precision and broader applicability compared to empirical formulas. Our study provides an effective tool for predicting the thermal conductivity of polymer composites with single fillers and underscores the potential of machine learning in accelerating materials design.

Waste-to-energy poly-generation scheme for hydrogen/freshwater/power/oxygen/heating capacity production; optimized by regression machine learning algorithms

Utilization of machine learning techniques in the analysis and enhancement of poly-generation energy systems improves their efficiency and sustainability. Also, waste-to-energy systems propose a hopeful answer for both waste management and sustainable energy and water production. The production of hydrogen and freshwater through these systems not only provides valuable resources but also contributes to environmental sustainability. This research utilizes artificial intelligence's machine learning algorithms to examine and enhance a waste-to-energy system within a poly-generation energy system that generates hydrogen, freshwater, power, oxygen, and hot air. The integrated system consists of a waste combustion chamber, a proton exchange membrane electrolyzer, a supercritical carbon dioxide Brayton cycle, and a desalination system. Based on the findings, the machine learning algorithms exhibiting R-squared values exceeding 99% are considered strong fits. Additionally, all algorithms with high predicted R-squared values have the capability to accurately forecast new data using the provided training data. The emissions of 669.7 g/kWh and an efficiency of 69.62% are achieved when the pressure ratio is 10.73 and the temperature is 863.6 °C under optimal conditions. The accuracy and validity of the machine learning techniques are further confirmed by the strong agreement with thermodynamic modeling.

Machine learning regression algorithms for predicting muscle, bone, carcass fat and commercial cuts in hairless lambs

The growth in demand and demand for quality in the sheep chain has generated the need for automation techniques in the meat industry and the need to obtain responses with greater speed and standardization. The research aimed to predict tissue characteristics of the carcass and commercial cuts based on measurements obtained by VIA – see oimage analysis, carried out on cold carcasses of hairless lambs, using machine learning employing regressive techniques for variable selection. Information from 72 carcasses of castrated male lambs, aged between 8 and 11 months, with an average cold carcass weight of 16.13 ± 3.98kg, was used. Images of the right side of the carcasses were captured from the dorsal and lateral views using a digital camera. From the ImageJ2 software, VIA data, measurements and shape descriptors (areas, perimeters, widths, lengths, convexities, solidities) were obtained, combined with cold carcass weight and used to generate four sets of data, called descriptor sets (DSs). Obtaining DS1, DS1’, DS2, DS2’, DS3, DS3’, DS4 AND DS4’. To generate these sets, a database was formed and divided into a training bank (with 70% of the observations) and a test bank (30% of the observations). Multiple linear regression models were developed using Stepwise, LASSO, and Elastic Net regression methods, combined with k-fold cross-validation, to evaluate the performance of the models. The accuracy of the estimates was based on RMSE, R2, Pearson correlation and bias metrics. For the variables tested in this study, the proposed shape descriptors were mostly efficient in predicting tissue and weight variables. DS1' with the LASSO technique presented the best adjustments for variables total muscle and fat followed by shoulder, loin and rib cuts. The descriptors tested by this study were able to predict with quality the vast majority of the characteristics tested, the variable cold carcass weight (CCW), introduced as additional predictor, promoted a consistent improvement in the fits of all models. DS1 presented greater constancy for the twenty-three predicted characteristics and Stepwise presented the worst predictive performance, in relation to LASSO and Elastic Net. Despite close adjustments between the generated models, in general, Elastic Net presented lower performance than LASSO.

An artificial intelligence study on energy, exergy, and environmental aspects of upcycling face mask waste to a hydrogen-rich syngas through a thermal conversion process

Studies in the field of gasification process for face mask wastes play a significant role in addressing the environmental issues associated with these wastes and harnessing their potential as an important energy source. On the other hand, machine learning techniques offer a promising approach to predict the environmental, energy, and exergy performances of the gasification process. This study addressed the utilization of machine learning techniques on the gasification process of face mask wastes. Various polynomial regression machine learning algorithms are developed for syngas lower heating value, exergy and cold gas efficiencies, and emission level and their performances are checked. Remarkable precision of machine learning algorithms in forecasting the response variables is evidenced by the impressive outcomes. In fact, the majority of cases yield R-sq values surpassing 98%, thus highlighting their effectiveness. Specifically, when it comes to exergy and cold gas efficiencies, and emission, the machine learning algorithms achieve exceptional R-sq values of 99.85%, 98.93%, and 99.33%, respectively. These exceptional results confirm the credibility and accuracy of these algorithms in providing precise predictions. Leveraging the power of artificial intelligence not only contributes to the development of more sustainable energy systems but also helps in making informed decisions regarding process design and operation.

INTEGRATION OF STUMPF'S RATIO MODEL AND RANDOM FOREST FOR SATELLITE-DERIVED BATHYMETRY ESTIMATION

Abstract. The development of remote sensing for coastal and marine environment mapping has significantly enhanced our understanding of these ecosystems, enabling improved mitigation strategies against the impacts of human activities. However, remote sensing must consider the complex interplay of the atmosphere and water column. Ongoing research focuses on refining water column correction techniques, including Depth Invariant Indices (DII), Radiative Transfer models, and bathymetry models. This study specifically aims to enhance the Stumpf's Ratio model (SRM) for bathymetry by employing the Random Forest (RF) machine learning regression algorithm. The resulting bathymetry model, which incorporates visible bands from a Sentinel-2 MSI image, and their Stumpf's ratios, outperforms other methods, yielding the highest accuracy with RMSE and R2 values of 1.25 m and 0.854, respectively. This was followed by the multivariate SRM with RMSE and R2 values of 2.196 m and 0.554 respectively These findings demonstrate the promising potential of using RF machine learning regression with SRM for bathymetry modelling.

Improved flood depth estimation with SAR image, digital elevation model, and machine learning schemes

Study regionNhat Le river basin, the floodplains of Central coast of Vietnam. Study focusFlood disasters have a significant impact on population and economies worldwide. To accurately assess flood damage, it is crucial to estimate the water depth and predict the potential spread of damage. However, conventional methods of estimating flood depth can be time-consuming and costly due to the large amount of data required from hydraulic and rain-runoff models. To overcome these limitations, we propose a new approach that integrates a machine learning (ML) algorithm with a floodwater depth estimation tool (FwDET) based on SAR imagery and digital elevation models (DEMs). To evaluate our approach, we trained and tested six ML regression algorithms using 804 sets of inventory flood depth data from the historic flood on the Nhat Le River in Vietnam in October 2020 as the dependent variable. This approach significantly improves flood depth prediction accuracy, enabling a faster and more cost-effective estimation of flood damage. New hydrological insights for the regionOur research shows that by combining ML with FwDET, we were able to significantly enhance water depth prediction accuracy. We observed a 25.05% increase in the R2 coefficient and a 13.46% increase in the R coefficient. We recommend integrating satellite imagery, DEM, and ML for flood depth predictions, especially for floodplains with similar topographical conditions as the Nhat Le basin. This approach offers a practical, cost-effective, and near real-time solution for flood response. Implementing this approach can help improve flood damage assessment and mitigate the potential impacts of flood disasters on communities and economies.

Machine learning regression algorithms for predicting the susceptibility of jointed rock slopes to planar failure

Machine learning regression algorithms for predicting the susceptibility of jointed rock slopes to planar failure

Autonomous flight performance optimization of fixed-wing unmanned aerial vehicle with morphing wingtip

PurposeThe purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.Design/methodology/approachThe main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.FindingsLongitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.Originality/valueThis paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

An advanced nomogram model using deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma

PurposeTo evaluate the effectiveness of deep learning radiomics nomogram in distinguishing the occult lymph node metastasis (OLNM) status in clinical stage IA lung adenocarcinoma. MethodsA cohort of 473 cases of lung adenocarcinomas from two hospitals was included, with 404 cases allocated to the training cohort and 69 cases to the testing cohort. Clinical characteristics and semantic features were collected, and radiomics features were extracted from the computed tomography (CT) images. Additionally, deep transfer learning (DTL) features were generated using RseNet50. Predictive models were developed using the logistic regression (LR) machine learning algorithm. Moreover, gene analysis was conducted on RNA sequencing data from 14 patients to explore the underlying biological basis of deep learning radiomics scores. ResultThe training and testing cohorts achieved AUC values of 0.826 and 0.775 for the clinical model, 0.865 and 0.801 for the radiomics model, 0.927 and 0.885 for the DTL-radiomics model, and 0.928 and 0.898 for the nomogram model. The nomogram model demonstrated superiority over the clinical model. The decision curve analysis (DCA) revealed a net benefit in predicting OLNM for all models. The investigation into the biological basis of deep learning radiomics scores identified an association between high scores and pathways related to tumor proliferation and immune cell infiltration in the microenvironment. ConclusionsThe nomogram model, incorporating clinical-semantic features, radiomics, and DTL features, exhibited promising performance in predicting OLNM. It has the potential to provide valuable information for non-invasive lymph node staging and individualized therapeutic approaches.

Machine learning-based prediction and experimental validation of heavy metal adsorption capacity of bentonite

As a natural adsorbent material, bentonite is widely used in the field of heavy metal adsorption. The heavy metal adsorption capacity of bentonite varies significantly in studies due to the differences in the properties of bentonite, solution, and heavy metal. To achieve accurate predictions of bentonite's heavy metal adsorption capacity, this study employed six machine learning (ML) regression algorithms to investigate the adsorption characteristics of bentonite. Finally, an eXtreme Gradient Boosting Regression (XGB) model with outstanding predictive performance was constructed. Explanation analysis of the XGB model further reveal the importance and influence manner of each input feature in predicting the heavy metal adsorption capacity of bentonite. The feature categories influencing heavy metal adsorption capacity were ranked in order of importance as adsorption conditions > bentonite properties > heavy metal properties. Furthermore, a web-based graphical user interface (GUI) software was developed, facilitating researchers and engineers to conveniently use the XGB model for predicting the heavy metal adsorption capacity of bentonite. This study provides new insights into the adsorption behaviors of bentonite for heavy metals, offering guidance and support for enhancing its application efficiency and addressing heavy metal pollution remediation.

Machine learning regression algorithms for generating chemical element maps from X-ray fluorescence data of paintings

Generating chemical element maps of paintings from X-ray fluorescence (XRF) data is a very valuable tool for the scientific community of conservators and art historians. Hand-held XRF scanners are cheap and easily portable but their use provides scans with a few data, so additional analytical tools are needed to obtain reliable chemical element maps from them. Recently, the software tool SmART_Scan was released, which uses an algorithm based on the minimum hypercube distance (MHD) to compute this kind of maps. In this paper, we propose a new methodology to address this problem by using machine learning algorithms for regression as alternative and more accurate techniques than MHD. We tested MHD versus eight machine learning regression algorithms on two paintings with different features. Our results showed that machine learning algorithms Random Forest and kNN significantly outperformed MHD in Mean Squared Error (MSE) and coefficient of determination (R2) for all the experiments. When using experts’ data and a hold-out validation, kNN was the best-ranked algorithm. Random Forest was the best-ranked algorithm when cross-validation was used. We did not find significant differences in average MSE nor in R2 between kNN and Random Forest, so we can conclude that Random Forest is the best-suited algorithm for computing chemical element maps of paintings from XRF data.

Estimating Winter Wheat Plant Nitrogen Content by Combining Spectral and Texture Features Based on a Low-Cost UAV RGB System throughout the Growing Season

As prior information for precise nitrogen fertilization management, plant nitrogen content (PNC), which is obtained timely and accurately through a low-cost method, is of great significance for national grain security and sustainable social development. In this study, the potential of the low-cost unmanned aerial vehicle (UAV) RGB system was investigated for the rapid and accurate estimation of winter wheat PNC across the growing season. Specifically, texture features were utilized as complements to the commonly used spectral information. Five machine learning regression algorithms, including support vector machines (SVMs), classification and regression trees, artificial neural networks, K-nearest neighbors, and random forests, were employed to establish the bridge between UAV RGB image-derived features and ground-truth PNC, with multivariate linear regression serving as the reference. The results show that both spectral and texture features had significant correlations with ground-truth PNC, indicating the potential of low-cost UAV RGB images to estimate winter wheat PNC. The H channel, S4O6, and R_SE and R_EN had the highest correlation among the spectral indices, Gabor texture features, and grey level co-occurrence matrix texture features, with absolute Pearson’s correlation coefficient values of 0.63, 0.54, and 0.69, respectively. When the texture features were used together with spectral indices, the PNC estimation accuracy was enhanced, with the root mean square error (RMSE) decreasing from 2.56 to 2.24 g/kg, for instance, when using the SVM regression algorithm. The SVM regression algorithm with validation achieved the highest estimation accuracy, with a coefficient of determination (R2) of 0.62 and an RMSE of 2.15 g/kg based on the optimal feature combination of B_CON, B_M, G_DIS, H, NGBDI, R_EN, R_M, R_SE, S3O7, and VEG. Overall, this study demonstrated that the low-cost UAV RGB system could be successfully used to map the PNC of winter wheat across the growing season.

Reliable paths prediction with intelligent data plane monitoring enabled reinforcement learning in SD-IoT

Legacy routing with conventional distributed networking in the Internet-of-Things (IoT) networks needs to work on efficiently handling service requests, leading to compromised Quality of Service (QoS) for user demands. In this research, we introduce Reliable routing based on Reinforcement learning in SD-IoT Networks (RRSN), an intelligent routing technique leveraging Software-Defined Networking (SDN) in IoT networks. RRSN utilizes a hybrid mechanism that combines Machine Learning (ML) and Reinforcement Learning (RL) within SDN’s centralized control to optimize QoS-aware reliable routing. In addition, network telemetry is utilized for intelligent network monitoring in the data plane to get the underlying network’s status efficiently. The proposed approach consists of three main modules: a Support Vector Regression (SVR) machine learning algorithm to predict link reliability, a traffic classifier for QoS-based traffic classification, and a reliability-aware reinforcement learning algorithm to compute optimal routing paths for IoT sensors. SVR algorithm has a high prediction accuracy, is lightweight, less susceptible to noisy data, and its computational complexity is independent of the dimensions of the input space. RRSN leverages intelligent network monitoring with RL, global view, and centralized SDN control to compute and implement optimal routes in data plane switches efficiently. We conduct extensive Proof-Of-Concept (POC) experimental evaluations, comparing RRSN with state-of-the-art models, including POC experiments in real Internet topologies. The results demonstrate that RRSN outperforms existing schemes in network performance metrics such as throughput, jitter, packet loss ratio, and delay, showcasing its effectiveness in improved QoS for IoT networks.

Machine learning determines stemness associated with simple and basal-like canine mammary carcinomas

Simple and complex carcinomas are the most common type of malignant Canine Mammary Tumors (CMTs), with simple carcinomas exhibiting aggressive behavior and poorer prognostic. Stemness is an ability associated with cancer initiation, malignancy, and therapeutic resistance, but is still few elucidated in canine mammary tumor subtypes. Here, we first validated, using CMT samples, a previously published canine one-class logistic regression machine learning algorithm (OCLR) to predict stemness (mRNAsi) in canine cancer cells. Then, using the canine mRNAsi, we observed that simple carcinomas exhibit higher stemness than complex carcinomas and other histological subtypes. Also, we confirmed that stemness is higher and associated with basal-like CMTs and with NMF2 metagene signature, a tumor-specific DNA-repair metagene signature. Using correlation analysis, we selected the top 50 genes correlated with higher stemness, and the top 50 genes correlated with lower stemness and further performed a gene set enrichment analysis to observe the biological processes enriched for these genes. Finally, we suggested two promise stemness-associated targets in CMTs, POLA2 and APEX1, especially in simple carcinomas. Thus, our work elucidates stemness as a potential mechanism behind the aggressiveness and development of canine mammary tumors, especially in simple carcinomas, describing evidence of a promising strategy to target this disease.

Machine learning regression algorithms to predict emissions from steam boilers

Currently, the modeling of complex chemical-physical processes is drastically influencing industrial development. Therefore, the analysis and study of the combustion process of the boilers using machine learning (ML) techniques are vital to increase the efficiency with which this equipment operates and reduce the pollution load they contribute to the environment. This work aims to predict the emissions of CO, CO2, NOx, and the temperature of the exhaust gases of industrial boilers from real data. Different ML algorithms for regression analysis are discussed. The following are input variables: ambient temperature, working pressure, steam production, and the type of fuel used in around 20 industrial boilers. Each boiler's emission data was collected using a TESTO 350 Combustion Gas Analyzer. The modeling, with a machine learning approach using the Gradient Boosting Regression algorithm, showed better performance in the predictions made on the test data, outperforming all other models studied. It was achieved with predicted values showing a mean absolute error of 0.51 and a coefficient of determination of 99.80%. Different regression models (DNN, MLR, RFR, GBR) were compared to select the most optimal. Compared to models based on Linear Regression, the DNN model has better prediction performance. The proposed model provides a new method to predict CO2, CO, NOx emissions, and exhaust gas outlet temperature.

Multicriteria Machine Learning Model Assessment—Residuum Analysis Review

The use of machine learning (ML) and its applications is one of the leading research areas nowadays. Neural networks have recently gained enormous popularity and many works in various fields use them in the hope of improving previous results. The application of the artificial intelligence (AI) methods and the rationale for this decision is one issue, but the assessment of such a model is a completely different matter. People mostly use mean square error or less often mean absolute error in the absolute or percentage versions. One should remember that an error does not equal an error and a single value does not provide enough knowledge about the causes of some behavior. Proper interpretation of the results is crucial. It leads to further model improvement. It might be challenging, but allows us to obtain better and more robust solutions, which ultimately solve real-life problems. The ML model assessment is the multicriteria task. A single measure delivers only a fraction of the picture. This paper aims at filling that research gap. Commonly used integral measures are compared with alternative measures like factors of the Gaussian and non-Gaussian statistics, robust statistical estimators, tail index and the fractional order. The proposed methodology delivers new single-criteria indexes or the multicriteria approach, which extend the statistical concept of the moment ratio diagram (MRD) into the index ratio diagram (IRD). The proposed approach is validated using real data from the Full Truck Load cost estimation example. It compares 35 different ML regression algorithms applied to that task. The analysis gives an insight into the properties of the selected methods, enables their comparison and homogeneity analysis and ultimately leads towards constructive suggestions for their eventual proper use. The paper proposes new indexes and concludes that correct selection of the residuum analysis methodology makes the assessment and the ML regression credible.

Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain.

Machine learning (ML) algorithms are crucial within the realm of healthcare applications. However, a comprehensive assessment of the effectiveness of regression algorithms in predicting alterations in lifting movement patterns has not been conducted. This research represents a pilot investigation using regression-based machine learning techniques to forecast alterations in trunk, hip, and knee movements subsequent to a 12-week strength training for people who have low back pain (LBP). The system uses a feature extraction algorithm to calculate the range of motion in the sagittal plane for the knee, trunk, and hip and 12 different regression machine learning algorithms. The results show that Ensemble Tree with LSBoost demonstrated the utmost accuracy in prognosticating trunk movement. Meanwhile, the Ensemble Tree approach, specifically LSBoost, exhibited the highest predictive precision for hip movement. The Gaussian regression with the kernel chosen as exponential returned the highest prediction accuracy for knee movement. These regression models hold the potential to significantly enhance the precision of visualisation of the treatment output for individuals afflicted with LBP.