Traditional Linear Model Research Articles

Cross-validated tree-based models for multi-target learning.

Multi-target learning (MTL) is a popular machine learning technique which considers simultaneous prediction of multiple targets. MTL schemes utilize a variety of methods, from traditional linear models to more contemporary deep neural networks. In this work we introduce a novel, highly interpretable, tree-based MTL scheme which exploits the correlation between the targets to obtain improved prediction accuracy. Our suggested scheme applies cross-validated splitting criterion to identify correlated targets at every node of the tree. This allows us to benefit from the correlation among the targets while avoiding overfitting. We demonstrate the performance of our proposed scheme in a variety of synthetic and real-world experiments, showing a significant improvement over alternative methods. An implementation of the proposed method is publicly available at the first author's webpage.

A predictive study on the impact of board characteristics on firm performance of Chinese listed companies based on machine learning methods

Purpose This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms. Design/methodology/approach This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance. Findings The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises. Practical implications The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors. Originality/value The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

Locally optimal tests against periodic linear regression in short panels

This paper aims to detect the periodic coefficients of a Linear Regression model with Panel Data. Nonparametric locally and asymptotically optimal tests are proposed for testing the null hypothesis of a traditional linear regression model, against an alternative of a Periodic Coefficient Regression model. The Uniform Local Asymptotic Normality (ULAN) property is established for Periodic Linear Regression (PLR) model, to construct Locally asymptotically optimal tests. The rank-based version of the optimal parametric test is provided in particular cases. The performance of the proposed tests is valid with numerical simulations using the Monte Carlo study. Real data was studied for applying the periodic linear regression model which compared to both traditional regression and random coefficient regression models.

How to make calibration less painful-A proposition for an automatic, reliable and time-efficient procedure.

In behavioral and neurophysiological pain studies, multiple types of calibration methods are used to quantify the individual pain sensation stimuli. Often, studies lack a detailed calibration procedure description, data linearity, and quality quantification and omit required control for sex pain differences. This hampers study repetition and interexperimental comparisons. Moreover, typical calibration procedures require a high number of stimulations, which may cause discomfort and stimuli habituation among participants. To overcome those shortcomings, we present an automatic calibration procedure with a novel stimuli estimation method for intraepidermal stimulation. We provide an in-depth data analysis of the collected self-reports from 70 healthy volunteers (37 males) and propose a method based on a dynamic truncated linear regression model (tLRM). We compare its estimates for the sensation (t) and pain (T) thresholds and mid-pain stimulation (MP), with those calculated using traditional estimation methods and standard linear regression models. Compared to the other methods, tLRM exhibits higher R2 and requires 36% fewer stimuli applications and has significantly higher t intensity and lower T and MP intensities. Regarding sex differences, t and T were found to be lower for females compared to males, regardless of the estimation method. The proposed tLRM method quantifies the calibration procedure quality, minimizes its duration and invasiveness, and provides validation of linearity between stimuli intensity and subjective scores, making it an enabling technique for further studies. Moreover, our results highlight the importance of control for sex in pain studies.

Linear time-varying fractional-order model predictive attitude control for satellite using two reaction wheels

Achieving precise attitude control in satellites equipped with two reaction wheels remains a significant challenge. During the last decade, model predictive control has emerged as a promising technique for satellite attitude control. However, this paper takes a step by introducing the novel concept of linear time-varying fractional-order model predictive control specifically tailored for satellites with dual reaction wheels, enabling them to achieve desired orientations more effectively. This study begins by elucidating the nonlinear dynamics and kinematic equations governing the satellite's behavior. To facilitate control design, the satellite linear time-varying model is derived by linearizing the nonlinear equations around the operating point. Leveraging this linearized representation, a linear time-varying fractional-order model predictive control method is designed, leveraging the benefits of fractional-order cost functions. Finally, the simulation results show the remarkable performance of the linear time-varying fractional-order model predictive control approach, particularly when confronted with input constraints. Notably, the proposed method outperforms the traditional linear time-varying model predictive control method by achieving an 80% improvement in tracking speed, while simultaneously reducing the applied torque by 67%. Additionally, the proposed approach decreases the mean absolute error of the satellite attitude angles by over 80% compared to the conventional linear time-varying model predictive control method. This research not only addresses the pressing challenge of attitude control in satellites with dual reaction wheels but also presents a novel and highly effective approach that surpasses existing methods in terms of performance and efficiency. As such, it holds immense potential for the field of satellite attitude control, making it an indispensable read for engineers and researchers working in the domain of aerospace engineering and control systems.

Predicting Microbiologically Influenced Concrete Corrosion in Self-Cleansing Sewers Using Meta-Learning Techniques

Sewer networks are not only necessary as an infrastructure for human societies, but they can also help humans achieve a stable situation with the surrounding natural environment by controlling and preventing the spread of pollution in the environment. As a result, concrete sewer maintenance and analysis of their damaging elements are critical. In this regard, modeling microbiologically influenced corrosion (MIC) is a challenging phenomenon. Due to the complicated aspects related to the interaction of microorganisms and concrete degradation, this research suggests several machine-learning models as well as traditional multiple linear regression model to predict the MIC in sewer pipelines. The models can be categorized into three sections: (i) stand-alone models (group method of data handling, generalized regression neural network, radial basis function neural network, multilayer perceptron neural network, chi-square automatic interaction detection, and classification and regression tree); (ii) integrative models (adaptive neuro-fuzzy inference system and support vector regression with particle swarm optimization, artificial bee colony, and firefly algorithm); and (iii) ensemble meta-learner stepwise regression (SR) model. After implementing the models, statistical measures, including root mean square error, mean absolute error, mean bias error, Pearson correlation coefficient, and Nash-Sutcliffe model efficiency are considered for evaluating models’ performances. The results indicate that the ensemble meta-learner-SR model is significantly more precise than other models. They also demonstrate that using an integrative model can improve the accuracy of stand-alone models by at least up to 42%. The durability and lifespan of the sewer system are also estimated with the aid of the best predictive model (meta-learner-SR) for two scenario cases of (i) gas phase and (ii) submerged conditions. It is concluded that the sewer systems have a considerably lower life span (24 y less) exposed to submerged sewage than the gas phase with 56 y of durability.

A novel model for measuring carbon dioxide flux on soil surface using an accumulation chamber

Soil surface carbon dioxide (CO2) fluxes hold significant implications for comprehending carbon cycling, global climate change, and ecosystem functioning. Accurate understanding and quantification of this flux are crucial for assessing the carbon dynamics of ecosystems and predicting the impacts of climate change. In this study, we analyzed the variation pattern of CO2 concentration within the accumulated chamber from Fick's law, accounting for diffusion gradient effect and considering leakage effect. We developed a simple model to measure the soil surface CO2 flux, and used the model to calculate the values of CO2 fluxes on a specific soil surface. Comparing the fitting results of the proposed nonlinear model with traditional linear models for the measurement data, we found that the linear model underestimated by at least 18%, even up to 45%. Overall, our research indicates that the proposed model exhibits significant advancement and higher predictive reliability, successfully evaluating soil surface CO2 flux.

Stress begets stress: The moderating role of childhood adversity in the relationship between job stress and sleep quality among nurses

Nurses exhibit considerable variations in sleep quality and experience high job stress levels. Distal factors, such as childhood adversity, and proximal factors, both influence sleep quality. We investigated the moderating role of childhood adversity with job stress and sleep quality, and whether this aligns with the stress-sensitization or stress-amplification models. The impact of job stressors' total score and its dimensions on sleep quality was analyzed using traditional linear regression models and the extreme gradient boosting machine learning algorithm. The hierarchical regression examined the moderating role of childhood adversity in the relationship between job stress and sleep quality. An interactive tool was used to visualize the results. Among the dimensions of job stress, "time allocation and workload" strongly correlated with sleep quality, followed by "nursing profession and work problems," "patient care issues," "management and interpersonal problems," and "working environment and equipment problems." The moderating role of childhood adversity in the relationship between different dimensions of job stressors (except working environment and equipment problems) and sleep quality aligns with the stress-sensitization model. This study was susceptible to recall bias and objective sleep data were unavailable. Cross-sectional study design was used, thus limiting causal inferences. Finally, the moderating effect of childhood adversity on subsequent stress among nurses remains unclear. Childhood adversity and job stress were integrated into a stress-sensitization model, providing a nuanced and specific examination of sleep quality. Healthcare policymakers should focus on job stress and childhood adversity, improve nurses' sleep quality, and ultimately benefit patient care and outcomes.

Modeling and controlling heat transfer in chambers: A comparative study of classical and intelligent approaches

This paper introduces nonlinear approaches which include neural networks and ANFIS to identify and control heat transfer within a chamber. Initially, traditional linear models are obtained using transfer functions with delays through Matlab identification tools. However, this traditional linear model failed to faithfully represent the system when the input was changed. This outcome was expected since linear models are reliable only within specific operational ranges. To create a novel model that is applicable across the entire state space, two alternative identification methods, utilizing neural networks and an adaptive neuro-fuzzy inference system were introduced. After testing them with input data not used during the training, the models were compared and all of them showed satisfiying results. In the continuation of the research, control techniques based on these techniques were presented. After assigning an arbitrary temperature as a reference signal, inverse models were made and four controllers in direct inverse control sheme were compared: three feedforward neural networks with different numbers of neurons in the hidden layer and the adaptive neuro-fuzzy inference controller. The results and possible improvements are discussed in the conclusion.

Quantitative analysis of spectral data based on stochastic configuration networks.

In quantitative analysis of spectral data, traditional linear models have fewer parameters and faster computation speed. However, when encountering nonlinear problems, their predictive accuracy tends to be lower. Nonlinear models provide higher computational accuracy in such situations but may suffer from drawbacks such as slow convergence speed and susceptibility to get stuck in local optima. Taking into account the advantages of these two algorithms, this paper introduces the single-hidden layer feedforward neural network named stochastic configuration networks (SCNs) into chemometrics analysis. Firstly, the model termination parameters, that is, the error tolerance and the allowed maximum number of hidden nodes are analyzed. Secondly, times of random configuration are discussed and analyzed, and then the appropriate number is determined by considering the efficiency and stability comprehensively. Finally, predictions made by the SCN are tested on two public datasets. The performance of the SCN is then compared with that of other techniques, including principal component regression (PCR), partial least squares (PLS), back propagation neural network (BPNN), and extreme learning machine (ELM). Experimental results show that the SCN has good stability, high prediction accuracy and efficiency, making it suitable for quantitative analysis of spectral data.

Fuzzy time series clustering using autoencoders neural network

<p>This study presents a novel approach that employs autoencoders (AE)—an artificial neural network—for the nonlinear transformation of time series to a compact latent space for efficient fuzzy clustering. The method was tested on atmospheric sea level pressure (SLP) data towards fuzzy clustering of atmospheric circulation types (CTs). CTs are a group of dates with a similar recurrent SLP spatial pattern. The analysis aimed to explore the effectiveness of AE in producing and improving the characterization of known CTs (i.e., recurrent SLP patterns) derived from traditional linear models like principal component analysis (PCA). After applying both PCA and AE for the linear and nonlinear transformation of the SLP time series, respectively, followed by a fuzzy clustering of the daily SLP time series from each technique, the resulting CTs generated by each method were compared to assess consistency. The findings reveal consistency between the SLP spatial patterns from the two methods, with 58% of the patterns showing congruence matches greater than 0.94. However, when examining the correctly classified dates (i.e., the true positives) using a threshold of 0.8 for the congruence coefficient between the spatial composite map representing the CT and the dates grouped under the CT, AE outperformed PCA with an average improvement of 29.2%. Hence, given AE's flexibility and capacity to model complex nonlinear relationships, this study suggests that AE could be a potent tool for enhancing fuzzy time series clustering, given its capability to facilitate the correct identification of dates when a given CT occurred and assigning the dates to the associated CT.</p>

A simple and efficient alginate hydrogel combined with surface-enhanced Raman spectroscopy for quantitative analysis of sodium nitrite in meat products.

Sodium nitrite is a commonly used preservative and color protectant in the food industry. Conventional analytical methods are highly susceptible to food matrix interference, time-consuming and costly. In this study, the ion cross-linking method was employed to prepare alginate hydrogel substrates, and phenosafranin was chosen as a single-molecule probe to analyze sodium nitrite. Our investigation centered on elucidating the effects of alginate and cross-linking ion concentrations on Raman signal characteristics. The optimal Raman response was observed in the precursor solution with 1% sodium alginate and 0.1 mol L-1 cross-linking ions. The relative standard deviations (RSDs) of the feature peaks from the three substrate batches ranged from 1.22% to 16.30%, attesting the robustness and consistency of the substrates. The signal reduction of the substrates after a four-week storage period remained below 10%, indicating that the substrates had good reproducibility and stability. The limits of detection (LODs) for sodium nitrite in extracts from cured meat, luncheon meat, and sliced ham were determined to range from 3.75 mg kg-1 to 8.11 mg kg-1, with low interference from the food matrix. The support vector machine algorithm was utilized to train and predict the data, which proved to be more accurate (98.6%-99.8% recovery) than the traditional linear regression model (81.9%-112.7% recovery) in predicting the spiked samples. The application of hydrogel-based surface-enhanced Raman spectroscopy (SERS) substrates for nitrite detection in food, combined with machine learning for regression prediction in data processing, collectively augmented the potential of SERS technology in the field of food analysis.

Performance optimization and experimental analysis of a gradient boosting tree-based model for real-time prediction of single gram weight of cigarettes

Abstract Accurate control of the gram weight of a single cigarette is of major significance for the improvement of product process quality and the reduction of production costs. This paper establishes a graph neural network (GNN) model to predict the equipment operation parameters and cigarette product weight indexes, based on data from the PROTOS1-8 fine cigarette rolling machine in a tobacco factory. Aiming at the lack of optimization ability of the GNNs model, using the excellent performance of the gradient boosting tree algorithm in nonlinear optimization ability and convergence speed, and using the cigarette gram weight index of the trained GNNs prediction model as the fitness function, we carry out the search for the optimal cigarette weight product indexes and come up with the set of operating parameters with the best cigarette weight indexes and optimize the cigarette equipment parameters in the reverse direction. Experiments and promotion tests have demonstrated that the method significantly enhances the optimization of fine cigarette equipment, with a prediction accuracy of 0.8942. Compared to the traditional multiple linear regression model of 0.8527, the prediction ability has improved by 4.15%. The method is useful for analyzing the parameters of different types of cigarette equipment and can meet practical needs.

The Asymmetric Effects of Exchange Rate Volatility on Domestic Investment in Asia

ABSTRACT: Exchange rate volatility is said to affect domestic investment in either direction. An increase in exchange rate volatility can affect domestic investment negatively since risks are higher, mostly due to volatile prices that are the results of exchange rate volatility. Most previous studies assumed that the response is symmetric. However, one study recently demonstrated that the effects of exchange rate volatility on domestic investment could be asymmetric. We add to this new asymmetry literature by assessing the short-run and long-run effects of exchange rate volatility on domestic investment in eight Asian countries. The linear Autoregressive Distributed Lag (ARDL) approach of Pesaran et al. (2001) is used to estimate the symmetric effects of exchange rate volatility on investment. We then follow Shin et al.’s (2014) approach of asymmetric cointegration and error-correction modelling to estimate asymmetric effects of exchange rate volatility on investment. The advantage of using both the approaches is that the variables could be a combination of I(0) and I(1), ruling out pre-unit root testing since these are the properties of almost all macroeconomic variables. Another advantage is that both approaches yield short-run and long-run results in one step. These advanced methods are applied to data from Hong Kong, Indonesia, Japan, South Korea, Malaysia, the Philippines, Singapore, and Thailand. We find that when a traditional linear and symmetric ARDL investment model was estimated for each of the eight countries in our sample, exchange rate volatility had a significant short-run effects on domestic investment in six countries. The short-run effects lasted into significant long-run effects only in the cases for Malaysia and Thailand. However, when a nonlinear model was estimated, the short-run asymmetric effects of volatility were found in all the eight countries. The short-run effects lasted into the long run only for Hong Kong, Indonesia, and Thailand. Clearly, more significant effects of exchange rate volatility were established by estimating a nonlinear model which must be attributed to the nonlinear adjustment of exchange rate volatility. These findings provide policy guidelines to investors in these countries as to how and in which direction exchange rate uncertainty will affect their investment decisions. They can then hedge against uncertain exchange rate volatility and maintain a stable investment environment.

A breakdown-free block conjugate gradient method for large-scale discriminant analysis

<abstract><p>Rayleigh-Ritz discriminant analysis (RRDA) is an effective algorithm for linear discriminant analysis (LDA), but there are some drawbacks in its implementation. In this paper, we first improved Rayleigh-Ritz discriminant analysis (IRRDA) to make its framework more concise, and established the equivalence theory of the solution space between our discriminant analysis and RRDA. Second, we proposed a new model based on positive definite systems of linear equations for linear discriminant analysis, and certificated the rationality of the new model. Compared with the traditional linear regression model for linear discriminant analysis, the coefficient matrix of our model avoided forming a centralized matrix or appending the original data matrix, but the original matrix itself, which greatly reduced the computational complexity. According to the size of data matrix, we designed two solution schemes for the new model based on the block conjugate gradient method. Experiments in real-world datasets demonstrated the effectiveness and efficiency of our algorithm and it showed that our method was more efficient and faster than RRDA.</p></abstract>

Simulation Study on Rain-Flood Regulation in Urban “Gray-Green-Blue” Spaces Based on System Dynamics: A Case Study of the Guitang River Basin in Changsha

Urban rainstorms and flood disasters are the most common and severe environmental problems worldwide. Many factors influence rain-flood control simulation, forming a complex network system of interconnected and mutually constraining elements. In terms of spatial scale selection, existing research on rain-flood disaster risk largely relies on a single-scale infrastructure index system and has not yet focused on urban “gray-green-blue” spatial scale simulations for rain-flood storage. Regarding research methodology, applying system dynamics methods to the simulation of rain-flood storage and disaster prevention planning in watershed cities is still in its initial stages. System dynamics models can simulate the feedback interactions among various sub-elements in the coupled mega-system, fully addressing complex issues within the system structure that involve multiple variables, non-linear relationships, and numerous feedback loops, thereby compensating for the inadequacies of traditional linear models in the collaborative management of rain-flood risks. Taking the Changsha Guitang River Basin as an example, this paper constructs a system dynamics model covering four dimensions: natural environment, socio-economics, internal structure, and policy development. It aims to derive the optimal planning scheme for gray-green-blue spatial coordination in rain-flood storage by weighing four different development scenarios. The simulation results show: (1) Simply changing the surface substrates without considering rainwater discharge and the plan that emphasizes the construction of municipal drainage facilities will see the capacity gap for rain-flood storage-space construction continue to widen by 2035. This indicates that the plans mentioned above will struggle to bear the socio-economic losses cities face during rain-flood disasters. (2) The plan of combining gray and green infrastructures sees the rain-flood storage construction capacity turn from negative to positive from 2024, rising to 52.259 billion yuan by 2035. This reflects that the plan can significantly reduce the rainwater volume in the later stages of low-impact development infrastructure construction, mitigate rain-flood disaster risks, and reduce government investment in rain-flood disaster risk management, making it a relatively excellent long-term rain-flood storage space planning option. (3) The rain-flood regulation space planning scheme, under the combined effect of the urban “gray-green-blue” network system, sees the capacity for rain-flood storage construction turn positive a year earlier than the previous plan, reaching 54.232 billion yuan by 2035. This indicates that the scheme can not only effectively respond to extreme flood and rainstorm disasters but also maintain ecological environment benefits and mitigate the socio-economic losses caused by disasters, making it the optimal choice for future government disaster management planning. The research results provide a theoretical framework and practical insights for territorial spatial planning, rain-flood control management, and resilient city construction in watershed areas.

Evaluating the Predictive Performance of Monthly Inflation Rates in Sri Lanka using the Hybrid Model (HB)

Aims/ objectives: This study develops and evaluates a novel hybrid model (HB) for forecasting monthly inflation rates in Sri Lanka, a country with a unique economic context, from 1988 to 2021. By integrating the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs), the study aims to overcome the limitations of traditional linear models in capturing the nonlinear patterns often observed in Sri Lankan economic data. Objectives: The study aims to assess the predictive accuracy of the HB model against established models, emphasizing its adaptability and robustness over a historically significant period. Methodology: Utilizing historical data, the study compares the HB model's forecasting performance with other established models, focusing on the Mean Absolute Percentage Error (MAPE) as a key metric of predictive accuracy. Results: The HB model demonstrates superior forecasting accuracy, with a notable reduction in MAPE to 7.10%, indicating its effectiveness in capturing the complexities of the Sri Lankan inflation trend. Conclusion: This study contributes to the field of economic forecasting by presenting a model that not only provides more accurate predictions but also adapts to the specific economic conditions of Sri Lanka. The findings have significant implications for economic planning and policy-making, highlighting the utility of hybrid forecasting models in developing economies.

Mathematical Modeling Using Multiple and Fully-Connected Linear Regressions

This article is devoted to the synthesis of traditional multiple linear regression models with fully connected linear regression models. It has been noted that these models, in a certain sense, complement each other — the disadvantages of multiple regression are compensated by the fully connected one, and the disadvantages of the fully connected one are compensated by the multiple one. Multiple regression estimates with partial multicollinearity of factors are unstable, and with complete multicollinearity they do not exist at all. There are no obstacles to the use of fully connected regression in such conditions, and its estimates, on the contrary, do not exist in the complete absence of linear dependence between factors. The estimated fully connected regression is an equation of a line in space, as opposed to a multiple regression, which is a hyperplane in space, so you can find estimates of a fully connected regression with only two observations at your disposal. An algorithm for estimating fully connected regressions using the maximum likelihood method is considered. Based on a sample size of 21, a multiple and fully connected linear regression of passenger rail traffic in the Irkutsk region was constructed, containing 23 input variables. During the construction process, it was possible to cope with multicollinearity and ensure protection of all input variables in the model. The constructed multiple and fully connected regression is adequate and fully satisfies the substantive meaning of the problem being solved, therefore its interpretation is given. Based on the results of the study, we can conclude that a combination of multiple and fully connected regressions can be very effective in solving data analysis problems.

The Estimation of Risk Premia with Omitted Variable Bias: Evidence from China

The Chinese stock market is replete with numerous omitted variables that can introduce biases in the standard estimation of risk premiums when traditional linear asset pricing models are applied. The three-pass method enables the estimation of risk premiums for observable factors even when not all relevant factors are explicitly specified or observed within the model. Accordingly, we have applied this method to construct portfolios with stocks from China’s A-share market as the test assets. Empirical research findings indicate that the three-pass method could be more effective than traditional linear asset pricing models in estimating risk premiums.

Iterative Development: Going with the Flow to Improve Care.

Healthcare can benefit immensely from implementing an Agile mindset. Specifically, Agile principles of value-driven delivery, adaptive planning, and continuous improvement can exert a positive impact on projects-especially amid the constant changes in laws, evidence-based practices, and technology in healthcare. The best way to apply Agile is to combine the discipline and structure of a traditional linear model with the adaptability of iterative development. That mix allows for certain controls but with the flexibility for stakeholders to provide input and call for adjustments at different stages. To start, an organization must build teams of multidisciplinary personnel with diverse perspectives. Delivering value should be addressed as soon as possible by considering stakeholder priorities, using tools for continuous project planning, and employing retrospectives to provide feedback and identify root causes. When leaders embrace value-driven delivery, adaptive planning, and continuous improvement, healthcare projects can lead to positive results and ultimately improve patient care.