Tree Model Research Articles

Factors associated with stair climbing independence at discharge in patients with vertebral compression fractures and their interrelationships: a study using decision tree analysis.

[Purpose] We aimed to examine factors at admission that are related to independence in stair climbing at discharge among patients with vertebral compression fractures. [Participants and Methods] The study included 179 female patients with vertebral compression fractures. A decision tree model was created to predict independence in stair climbing at discharge based on Dementia Scale-Revised, skeletal muscle mass index body mass index, grip strength, number of vertebral fractures, and number of injuries at admission. [Results] Analysis with the decision tree model showed that skeletal muscle mass index at admission, age, and grip strength were predictors for independence in stair climbing at discharge. [Conclusion] Patients with vertebral compression fractures who have a low skeletal muscle mass index and grip strength on admission may require assistance with stair climbing upon discharge.

Predicting tensile and fracture parameters in polypropylene-based nanocomposites using machine learning with sensitivity analysis and feature impact evaluation

This study examines the efficacy of decision tree and AdaBoost algorithms in predicting mechanical and fracture parameters of polypropylene nanocomposites toughened with ethylene-based and propylene-based thermoplastic elastomers and reinforced with fumed silica and halloysite nanotube nanoparticles. The essential work of the fracture approach was utilized to study the fracture parameters, including elastic and plastic works of the blended polymer nanocomposites. The data were divided into 80 % for training and 20 % for testing. AdaBoost consistently achieved superior performance compared to the decision tree model in all variables throughout both the training and testing stages. During the testing phase, the AdaBoost model obtained R2 values of 0.90 for Young's modulus, 0.93 for elongation at break, 0.87 for tensile strength, 0.86 for plastic work, and 0.60 for elastic work. Also, the mean absolute percentage error for the AdaBoost model during the test phase was 3.10 % for Young's modulus, 3.25 % for tensile strength, 10.34 % for elastic work, 13.55 % for plastic work, and 24.78 % for elongation at break. Furthermore, a sensitivity analysis examining the effects of various features such as TPO type, nanoparticles, and nanoparticle type on mechanical properties reveals that TPO has the most significant overall influence. The results also include an analysis of the impact of the key features on each mechanical property based on the sensitivity analysis.

Inherently interpretable machine learning for credit scoring: Optimal classification tree with hyperplane splits

An accurate and interpretable credit scoring model plays a crucial role in helping financial institutions reduce losses by promptly detecting, containing, and preventing defaulters. However, existing models often face a trade-off between interpretability and predictive accuracy. Traditional models like Logistic Regression (LR) offer high interpretability but may have limited predictive performance, while more complex models may improve accuracy at the expense of interpretability. In this paper, we tackle the credit scoring problem with imbalanced data by proposing two new classification models based on the optimal classification tree with hyperplane splits (OCT-H). OCT-H provides transparency and easy interpretation with ‘if-then’ decision tree rules. The first model, the cost-sensitive optimal classification tree with hyperplane splits (CSOCT-H). The second model, the optimal classification tree with hyperplane splits based on maximizing F1-Score (OCT-H-F1), aims to directly maximize the F1-score. To enhance model scalability, we introduce a data sample reduction method using data binning and feature selection. We then propose two solution methods: a heuristic approach and a method utilizing warm-start techniques to accelerate the solving process. We evaluated the proposed models on four public datasets. The results show that OCT-H significantly outperforms traditional interpretable models, such as Decision Trees (DT) and Logistic Regression (LR), in both predictive performance and interpretability. On certain datasets, OCT-H performs as well as or better than advanced ensemble tree models, effectively narrowing the gap between interpretable models and black-box models.

Optimisation of parameters of a dual-axis soil remediation device based on response surface methodology and machine learning algorithm

To accelerate the recycling of black soil, it is necessary to develop a new type of soil remediation equipment to improve its working efficiency. The one-way test was used to determine the mean level value of the steepest climb test, and the combined equilibrium method was used to determine the upper and lower interval levels of the response surface test for parameter optimisation. Based on the results of the response surface indices, machine learning was performed and the optimal model was determined. The results show that the predictive ability and stability of the decision tree model for the two indicators are better than that of random forest and support vector machine. The optimal parameter combinations determined using the decision tree model are: speed 73 rpm, homogenisation pitch 183 mm, homogenisation time 1 s, descent speed 0.06 m/s. The error between the optimal value of the machine learning prediction model and the actual simulation is 1.1% and 5.72%, respectively. The results of the study show that the effect of optimising the parameters through machine learning achieves a satisfactory prediction accuracy.

Predictive Factors of Chemotherapy-Induced Peripheral Neuropathy in Breast Cancer: A Decision Tree Model Approach.

Chemotherapy-induced peripheral neuropathy (CIPN) poses a substantial challenge in breast cancer (BC) chemotherapy, affecting the patient's quality of life. Recent studies have focused on exploring predictors and patterns of CIPN occurrence. We aimed to develop a prediction model for CIPN occurrence using a classification and regression tree (CART) algorithm. In this prospective study of 170 patients with BC undergoing chemotherapy, patient-reported adaptation of the Common Terminology Criteria for Adverse Events version 4.0 was used to assess CIPN occurrence. Multivariable analysis using the CART model was tuned using 10-fold cross-validation to identify baseline predictors for CIPN throughout chemotherapy. A receiver operating characteristic curve analysis was conducted for the CART model. A multivariable logistic regression was conducted from the variables included in the CART model to assess the strength and direction of the association. The prevalence of CIPN was 64.7% (n = 110). The most decisive predictor of CIPN occurrence in the CART model was the subject's C-reactive protein (CRP) level. CRP level >3.91 mg/dL, BMI >21.85 kg/m2, and a marital status of unmarried have predicted a probability of 100% in CIPN occurrence. The CART model showed an accuracy of 65.9%, sensitivity of 51.7%, specificity of 73.2%, and an area under the curve of 0.705. A CRP level of >3.91 mg/dL and a neutrophil-to-lymphocyte ratio (NLR) of >2.82 are significantly associated with the occurrence of CIPN (odds ratio [OR], 2.01 [95% CI, 1.01 to 4.01]; P = .046, OR, 2.09 [95%, CI, 1.02 to 4.24]; P = .042, respectively). Baseline CRP, NLR, BMI level, and marital status are significant predictors of CIPN occurrence throughout chemotherapy. Our CART model was better at ruling out individuals who would not experience CIPN. The CART model may provide insight into the future development of individualized patient care and prevention strategies.

Comparative Analysis of Decision Tree and Logistic Regression Models in Employee Recruitment and Selection for Enterprise Success

Enterprise success is determined by competent Human Resources (HR). The recruitment and selection process of employee candidates plays an important role in producing competent human resources as an effective initial selection increases the chances of finding the right candidate for a particular role. This research predicts the likelihood of a candidate being further selected in the interview phase based on behavioral and functional recruitment and selection which are important aspects of a candidate's potential fit and contribution to the enterprise. The research uses a comparison of decision tree analysis models and logistic regression to make predictions with several measurement metrics to see the accuracy and confusion matrix of each model used. Based on evaluation and validation, the decision tree analysis model is superior in prediction even though the results tend to be the same as the logistic regression model. The accuracy value of the classification using the decision tree model was 86.67% with correct prediction results of 78 data from 90 testing data and the accuracy value of the logistic regression model was 85.55% with correct prediction results of 77 data from 90 testing data. The results of the comparison of the two models show that the performance of the decision tree classifier model tends to be better.

Identifying Changes and Their Drivers in Paddy Fields of Northeast China: Past and Future

Northeast China plays a crucial role as a major grain-producing region, and attention to its land use and land cover changes (LUCC), especially farmland changes, are crucial to ensure food security and promote sustainable development. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) data and a decision tree model, land types, especially those of paddy fields in Northeast China from 2000 to 2020, were extracted, and the spatiotemporal changes in paddy fields and their drivers were analyzed. The development trends of paddy fields under different future scenarios were explored alongside the Coupled Model Intercomparison Project Phase 6 (CMIP6) data. The findings revealed that the kappa coefficients of land use classification from 2000 to 2020 reached 0.761–0.825, with an overall accuracy of 80.5–87.3%. The proposed land classification method can be used for long-term paddy field monitoring in Northeast China. The LUCC in Northeast China is dominated by the expansion of paddy fields. The centroids of paddy fields gradually shifted toward the northeast by a distance of 292 km, with climate warming being the main reason for the shift. Under various climate scenarios, the temperature in Northeast China and its surrounding regions is projected to rise. Each scenario is anticipated to meet the temperature conditions necessary for the northeastward expansion of paddy fields. This study provides support for ensuring sustainable agricultural development in Northeast China.

Assessing the Economic Feasibility of Needle-Retractable Safety Syringes (NRSS) Adoption in Indian Healthcare Facilities

Description: Globally, sixteen billion injections are given each year, 95.00% of which are for medicinal purposes. This global injection burden is accounted for by India between 24.5% and 31.0%, and alarmingly, over 62.50% of these injections are performed improperly or are repeated. Objectives: The purpose of this study is to assess the increased costs incurred by transitioning from traditional disposable syringes to Needle-Retractable Safety Syringes (NRSS) for beneficial usage in India. Methods: Utilizing a decision tree model, we evaluated the occurrence of needle-stick injuries and syringe reuse among healthcare personnel and patients. We calculated the lifetime costs associated with HIV - Human Immunodeficiency Virus, HBV - Hepatitis B Virus, and HCV - Hepatitis C Virus infections using three Markov models. Results: NRSS offer substantial benefits by reducing needle-stick injuries and preventing blood-borne infections. Among the options analyzed, Reuse Prevention (RUP) syringes emerged as the most cost-effective choice. In contrast, Single-Use Prevention (SIP) and NRSS syringes are currently not deemed cost-effective at their prevailing prices. We recommend prioritizing RUP syringes and exploring strategies to make them more affordable for broader accessibility. Conclusion: Our analysis suggests that, in the Indian context, RUP syringes present an economically viable solution. However, SIP and NRSS syringes do not currently offer cost-effectiveness. To enhance the cost-effectiveness of NRSS, concerted efforts should be directed towards reducing their price. Abbreviations: NRSS, Needle-Retractable Safety Syringes; RUP, Reuse Prevention; SIP, Single-Use Prevention; NSI, Needle Stick Injuries; BBI, Blood-borne Infections; QOL, Quality-of-life; BCC, Behavior Change Communication.

Regression of Concurrence via Local Unitary Invariants

Concurrence is a crucial entanglement measure in quantum theory used to describe the degree of entanglement between two or more qubits. Local unitary (LU) invariants can be employed to describe the relevant properties of quantum states. Compared to quantum state tomography, observing LU invariants can save substantial physical resources and reduce errors associated with tomography. In this paper, we use LU invariants as explanatory variables and employ methods such as multiple regression, tree models, and BP neural network models to fit the concurrence of 2-qubit quantum states. For pure states and Werner states, by analyzing the correlation between data, a functional formula for concurrence in terms of LU invariants is obtained. Additionally, for any two-qubit quantum states, the prediction accuracy for concurrence reaches 98.5%.

Heart Disease Prediction Using Decision Tree Analysis

Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. Therefore, if it is possible to predict diseases that has high mortality rate with patient's data and AI system, they would enable them to be detected and be treated in advance. The objective of the research is to use significant features and factors to design a prediction algorithm using Machine learning. The goal is to accurately classify whether a person has heart disease or not. The dataset contains 1025 records. The records are divided into two categories positive those who have heart disease and negative those who do not. It is currently standard practice to divide the data at random into roughly 70% for training and 30% for testing. The researcher used the MATLAB software tool to create a decision tree model of heart disease prediction. Using a decision tree model, it was determined that the best indicator that someone has heart disease is chest pain.

Fruit and Vegetable Image Recognition Based on Multiple Tree Models: Applications of Random Forest, XGBoost and Decision Tree

The primary objective of this study is to evaluate and compare the performance of three machine learning models— Random Forest, XGBoost, and Decision Tree—in the context of fruit and vegetable image classification. This research aims to identify which model best handles the challenges associated with imbalanced datasets and complex data structures. The ultimate goal is to contribute to the development of more efficient and accurate automated systems for agricultural applications, thereby improving productivity and reducing operational costs in the industry. This study utilized a dataset of 3,825 images covering 36 fruit and vegetable classes. Images were resized, normalized, and augmented to enhance diversity. Three models—Random Forest, XGBoost, and Decision Tree—were trained on this dataset. Performance was evaluated using accuracy, precision, recall, and F1-score to assess classification effectiveness and handling of class imbalances. The evaluation revealed that XGBoost outperformed Random Forest and Decision Tree in fruit and vegetable image classification, achieving the highest accuracy of 96.66%. XGBoost demonstrated superior handling of class imbalances and complex data structures, reflected in its precision and recall scores across various classes. Random Forest also performed well, closely following XGBoost, while Decision Tree exhibited more variability in results, indicating potential overfitting in certain classes. In conclusion, this study highlights the effectiveness of ensemble methods, particularly XGBoost, in agricultural image classification tasks. These findings suggest that XGBoost is a robust model for similar applications, offering improved accuracy and reliability

PREDICTIVE TECHNIQUES AND ARTIFICIAL INTELLIGENCE MODELS IN THE MANAGEMENT OF DEFAULTY PUBLIC DEBTS: COMPARISON BETWEEN LINEAR REGRESSION AND DECISION TREES

The study analyzed focuses on the application of predictive models, specifically Linear Regression and Decision Trees, for the management of defaulted debts in the public context of the United States. The main objective of the work is to compare the effectiveness of these models in predicting the compliance of debts with more than 120 days, assisting in directing these debts to the Treasury Offset Program (TOP), an essential initiative for the government's financial recovery. The problem that the study addresses is the need for effective management of defaulted public debts, seeking to ensure compliance with public financial policies that promote compliance and the adequate redirection of financial resources to the government. This is particularly important to ensure fiscal transparency and accountability of federal agencies. The methodology used in the study was quantitative, based on the analysis of data on eligible debts extracted from reports of the US Treasury. Linear Regression and Decision Tree models were applied, with performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R²). The study addressed financial and temporal variables to analyze the behavior of these debts and their compliance. The main results show that both models presented high accuracy in predictions, with Linear Regression showing a perfect fit (R² = 1) and Decision Trees standing out in capturing non-linear nuances of the data. The variable "Compliance Rate Amount" was identified as the most significant in the Decision Tree model, suggesting that the amount of the compliance rate is one of the most important factors in predicting the compliance of defaulted debts. This study offers valuable contributions to the field of public management, by demonstrating that the use of predictive models can help optimize debt recovery, improve fiscal transparency and contribute to more informed decision-making.

Enhancing Heart Disease Prediction through Machine Learning: A Comparative Analysis of Algorithm Generalization across Datasets with Various Distributions

Heart disease, due to its high prevalence and mortality, remains a key area of global research. Although traditional diagnostic methods are effective, they are often invasive and time-consuming, highlighting the need for non-invasive, AI-based approaches. A significant challenge in real-world applications is ensuring model generalization across different datasets, particularly when the datasets are small. In this study, the performance of machine learning models, including Decision Tree, Random Forest, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP), was evaluated on two distinct heart disease datasets with different distributions and relatively small sizes. Two datasets with varying distributions were used for training and testing, with the primary focus on assessing model generalization in crossdataset applications. It is shown in the results that, while the Decision Tree model performed best after hyperparameter tuning, the improvements in Random Forest and MLP were limited, and SVM exhibited a decline in performance after tuning in the cross-dataset task. It was found that grid search tuning has limitations in cross-dataset scenarios, especially with small datasets, where complex models are prone to overfitting. The study demonstrates that, with smaller datasets, simpler models like Decision Trees often adapt better to different datasets. Furthermore, transfer learning and domain adaptation techniques are suggested as crucial for improving model generalization. Future research should focus on employing these techniques to enhance the robustness and accuracy of heart disease prediction models across diverse datasets.

Developing a decision tree model to forecast runup and assess uncertainty in empirical formulations

The coastal zone is a dynamic region that can change rapidly and significantly with respect to the morphology of the beach and incoming wave conditions. Runup forecasts may be improved by adapting a dynamic approach that allows for different runup models to be implemented in response to changes in beach state. Accurately forecasting wave runup is critical to characterize exposure to coastal hazards and provide an early warning against potential erosion and inundation. Here, we developed a decision tree model to produce a weighted ensemble of existing runup models to predict 1.25 years of runup at Duck, North Carolina, USA. We then applied the calibrated decision tree model to reproduce observed runup during the DUNEX experiment in Pea Island, North Carolina, USA. We found that the decision tree approach yielded a prediction that was comparable or greater in accuracy (i.e. higher r2, lower RMSE) than the individual runup models. We also interrogated the decision tree predictions to determine how the individual models perform relative to each other and why certain models perform better than others under the same observed wave and beach conditions. We found that the decision tree approach drew on the processes represented in the individual models in the ensemble to produce a forecast that is accurate and explainable without relying on prior knowledge of the study site(s) or requiring manual adjustments beyond the initial model training.

Built Environment’s Non-Linear Impact on Subway Passenger Flow Through Improved Interpretable Machine Learning

Understanding the complex correlation between the built environment and subway passenger flow can provide unique insights for the development of transportation operations and urban coordination policies. Few studies have systematically analyzed the rationality of selecting built environment variables and further explored the non-linear relationships. In this study, we integrated various sources of built environmental factors and developed an interpretable machine learning analysis framework using backward elimination extreme gradient boosting and SHapley Additive exPlanations (SHAP) values analysis (BE-XGBoost-SHAP). The framework was validated by analyzing passenger flows during the morning peak, non-peak, and evening peak periods at the station level. The research results indicate that there are significant differences between built environment factors and the time-varying passenger flow. Land use characteristics significantly dominate across all three temporal periods. The importance of other variable types in relation to passenger flows varies significantly across the three time periods. It is worth noting that the relationships between all variables and passenger flow at different time periods are non-linear, with the majority displaying threshold effects. Compared with the gradient boosting decision tree (GBDT) and ordinary least squares (OLS) models, the proposed interpretive framework performs better as regards R-square, root mean square error (RMSE), and mean absolute error (MAE) metrics. This study offers valuable insights, elucidating the pivotal land use attributes that notably affect passenger flow, the significance of varied built environment factors across distinct time spans, and the acknowledgment of non-linearities and threshold effects within these relationships. These findings are imperative for urban planning and the enhancement of station area design.

Screening for congenital Chagas disease in a non-endemic area: a cost-effectiveness analysis

Abstract Background Due to climate changes, immigration, and globalization, Chagas Disease (CD) also afflicts nations without vector transmission. In Europe, congenital CD (cCD) is a significant public health issue, perpetuating the disease: it is crucial to perform CD screening in pregnant women at risk, as the treatment of infants is effective in eradicating the infection. Only a few European regions have a definite algorithm for diagnosing cCD, but no official guidance has been published at the national or European level. To support policymakers, this study aims to outline a cost-effectiveness analysis of CD screening in non-endemic areas, such as Italy, in pregnant women born or arriving from endemic countries and in their newborns. Methods To measure the economic impact of cCD screening, a decision tree model will be used to compare the test option (screening, diagnosis, treatment, follow-up) with no test option (no screening, progression of disease). Model parameters will be taken from relevant scientific literature. The primary outcome will be the Incremental Cost-Effectiveness Ratio (ICER) between the two options. Sensitivity analyses will be conducted to determine how changes in prevalence, transmission rate, screening adherence, and treatment adherence may affect the ICER. Results We expect the ‘test’ option to be cost-effective even with significant reductions in prevalence, transmission rate, adherence to screening or treatment. Threshold levels of these variables will allow to critically extend the results to areas with reduced immigration, patchy distribution of migrants at risk, health logistical limitations, and limited access to care for immigrants. Conclusions Our findings can help policymakers implement official screening programs and diagnostic algorithms for cCD in Italy and other non-endemic countries. This contributes to SDG 3 by combating the spread and burden of neglected tropical diseases and contributing to maternal, child, and migrant health. Key messages • Screening for congenital Chagas Disease in a non-endemic area may be cost-effective and cost-saving. • Study findings could provide useful information for health policymakers to combat Chagas Disease in Europe.

Research on Predicting the Winning Rate of Momentum Tennis Athletes

In tennis, "momentum" usually refers to the momentum or trend formed by a series of events (such as consecutive scoring) in a game, which can have a significant impact on the outcome of the game. After gaining motivation, players or teams often exhibit higher morale and confidence, and are more likely to win consecutive points or games. Specifically, momentum may be reflected in indicators such as continuous scores and break points. In a game, the server is usually more likely to win points, so evaluating momentum requires considering the influence of various factors. The paper believe that athletes are primarily influenced by the psychological impact of momentum, but psychological factors are also an abstract concept. Therefore, the paper have turned our attention to the various behaviors of athletes on the field. Therefore, the paper used five indicators to quantify the momentum score of athletes. To address the coach's concerns, the paper use random testing and correlation analysis to verify the statistical significance of attempting to score and win games, rather than random events. This article uses learning algorithms such as decision tree models to predict the value of target variables at turning points based on feature data. Analyze the contribution of each feature to advantage transformation and ultimately identify the relevant factors that affect advantage transformation.

Thyroglobulin measurement is the most powerful outcome predictor in differentiated thyroid cancer: a decision tree analysis in a European multicenter series.

An accurate prognostic assessment is pivotal to adequately inform and individualize follow-up and management of patients with differentiated thyroid cancer (DTC). We aimed to develop a predictive model for recurrent disease in DTC patients treated by surgery and 131I by adopting a decision tree model. Age, sex, histology, T stage, N stage, risk classes, remnant estimation, thyroid-stimulating hormone (TSH), thyroglobulin (Tg), administered 131I activities and post-therapy whole body scintigraphy (PT-WBS) were identified as potential predictors and put into regression algorithm (conditional inference tree, c-tree) to develop a risk stratification model for predicting persistent/recurrent disease over time. The PT-WBS pattern identified a partition of the population into two subgroups (PT-WBS positive or negative for distant metastases). Patients with distant metastases exhibited lower disease-free survival (either structural, DFS-SD, and biochemical, DFS-BD, disease) compared to those without metastases. Meanwhile, the latter were further stratified into three risk subgroups based on their Tg values. Notably, Tg values >63.1 ng/mL predicted a shorter survival time, with increased DFS-SD for Tg values <63.1 and <8.9 ng/mL, respectively. A comparable model was generated for biochemical disease (BD), albeit different DFS were predicted by slightly different Tg cutoff values (41.2 and 8.8 ng/mL) compared to DFS-SD. We developed a simple, accurate and reproducible decision tree model able to provide reliable information on the probability of structurally and/or biochemically persistent/relapsed DTC after a TTA. In turn, the provided information is highly relevant to refine the initial risk stratification, identify patients at higher risk of reduced structural and biochemical DFS, and modulate additional therapies and the relative follow-up.

Physical Frailty Prediction Using Cane Usage Characteristics during Walking.

This study aimed to determine the characteristics of accelerations and angular velocities obtained by an inertial measurement unit (IMU) attached to a cane between older people with and without physical frailty. Community-dwelling older people walked at a comfortable speed using a cane with a built-in IMU. Physical frailty was assessed using exercise-related items extracted from the Kihon Check List. The efficacy of five machine learning models in distinguishing older people with physical frailty was investigated. This study included 48 older people, of which 24 were frail and 24 were not. Compared with the non-frail participants, the older people with physical frailty had a small root mean square value in the vertical and anteroposterior directions and angular velocity in the anteroposterior direction (p < 0.001, r = 0.36; p < 0.001, r = 0.29; p < 0.001, r = 0.30, respectively) and a large mean power frequency value in the vertical direction (p = 0.042, r = 0.18). The decision tree model could most effectively classify physical frailty, with an accuracy, F1 score, and area under the curve of 78.6%, 91.8%, and 0.81, respectively. The characteristics of IMU-attached cane usage by older adults with physical frailty can be utilized to effectively evaluate and determine physical frailty in their usual environments.

Cost-effectiveness analysis of implementing the Nagasaki Acute Myocardial Infarction (AMI) secondary prevention clinical pathway

Abstract Background/Introduction Because of the poor prognosis associated with acute coronary syndromes (ACS), intensive low-density lipoprotein cholesterol (LDL-C) management therapy is recommended as early as possible after the onset of ACS, with the goal of lowering plasma LDL-C to &amp;lt;70 mg/dL. In Nagasaki City, a council was formed comprising all hospitals performing percutaneous coronary intervention along with general physicians, and an initiative to manage LDL-C using a region-wide LDL-C management clinical pathway (Figure 1) was launched in July 2022. The rate of intensive statin therapy at discharge increased from 34.5% to 79.8%, and the rate of patients achieving LDL-C &amp;lt;70 mg/dL at discharge increased from 37.2% to 54.6% following the implementation of the clinical pathway. However, the cost-effectiveness of implementing the clinical pathway remains to be determined. Purpose: The purpose of this study was to evaluate the cost-effectiveness of implementing the Nagasaki AMI Secondary Prevention Clinical Pathway. Methods: To evaluate the cost-effectiveness of implementing the clinical pathway compared with no implementation, a lifetime simulation was performed using mathematical models. The models consisted of a decision tree model to model the flow of the clinical pathway (Figure 2) and a Markov model to consider the risks of developing cardiovascular disease (CVD) events. The quality-adjusted life year (QALY) was used as the outcome. Drug costs and treatment costs for each CVD event were included. Drug costs were calculated from actual prescriptions. Treatment costs and utility scores for each event were based on the literature. The risk of each CVD event was calculated using baseline risks estimated using commercial claims data (DeSC Healthcare, Inc.) and patients' LDL-C levels at discharge and 1 month after discharge. LDL-C levels at each time point were set separately for the groups that achieved or did not achieve a discharge LDL-C &amp;lt; 70 mg/dL, for patients with and without implementing the clinical pathway. Results: Implementing the clinical pathway was evaluated as dominant, with an incremental gain of 0.079 QALYs per person and expected cost savings of JPY 103,124 per person. Implementing the clinical pathway was shown to not only provide additional health benefits by preventing CVD, but also to reduce future health care costs. In the analysis with a 10-year time horizon, the result remained dominant, and the same level of cost savings was expected. Conclusions: Implementing the Nagasaki AMI Secondary Prevention Clinical Pathway can be expected to reduce costs, in addition to increasing QALYs by preventing CVD. Further clinical and economic evaluation of long-term follow-up with a collaborative pathway to the general physician after discharge from the hospital is expected.