Decision Tree Model Research Articles

Securing transactions: a hybrid dependable ensemble machine learning model using IHT-LR and grid search

Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial for identifying and preventing unauthorized transactions. While credit card fraud incidents are relatively rare, they can result in substantial financial losses, particularly due to the high monetary value associated with fraudulent transactions. Timely detection of fraud enables investigators to take swift actions to mitigate further losses. However, the investigation process is often time-consuming, limiting the number of alerts that can be thoroughly examined each day. Therefore, the primary objective of a fraud detection model is to provide accurate alerts while minimizing false alarms and missed fraud cases. In this paper, we introduce a state-of-the-art hybrid ensemble (ENS) dependable machine learning (ML) model that intelligently combines multiple algorithms with proper weighted optimization using grid search, including decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), to enhance fraud identification. To address the data imbalance issue, we employ the instant hardness threshold (IHT) technique in conjunction with logistic regression (LR), surpassing conventional approaches. Our experiments are conducted on a publicly available credit card dataset comprising 284,807 transactions. The proposed model achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a perfect 100% for the DT, RF, KNN, MLP and ENS models, respectively. The hybrid ensemble model outperforms existing works, establishing a new benchmark for detecting fraudulent transactions in high-frequency scenarios. The results highlight the effectiveness and reliability of our approach, demonstrating superior performance metrics and showcasing its exceptional potential for real-world fraud detection applications.

Using interpretable machine learning methods to identify the relative importance of lifestyle factors for overweight and obesity in adults: pooled evidence from CHNS and NHANES

BackgroundOverweight and obesity pose a huge burden on individuals and society. While the relationship between lifestyle factors and overweight and obesity is well-established, the relative contribution of specific lifestyle factors remains unclear. To address this gap in the literature, this study utilizes interpretable machine learning methods to identify the relative importance of specific lifestyle factors as predictors of overweight and obesity in adults.MethodsData were obtained from 46,057 adults in the China Health and Nutrition Survey (2004–2011) and the National Health and Nutrition Examination Survey (2007–2014). Basic demographic information, self-reported lifestyle factors, including physical activity, macronutrient intake, tobacco and alcohol consumption, and body weight status were collected. Three machine learning models, namely decision tree, random forest, and gradient-boosting decision tree, were employed to predict body weight status from lifestyle factors. The SHapley Additive exPlanation (SHAP) method was used to interpret the prediction results of the best-performing model by determining the contributions of specific lifestyle factors to the development of overweight and obesity in adults.ResultsThe performance of the gradient-boosting decision tree model outperformed the decision tree and random forest models. Analysis based on the SHAP method indicates that sedentary behavior, alcohol consumption, and protein intake were important lifestyle factors predicting the development of overweight and obesity in adults. The amount of alcohol consumption and time spent sedentary were the strongest predictors of overweight and obesity, respectively. Specifically, sedentary behavior exceeding 28–35 h/week, alcohol consumption of more than 7 cups/week, and protein intake exceeding 80 g/day increased the risk of being predicted as overweight and obese.ConclusionPooled evidence from two nationally representative studies suggests that recognizing demographic differences and emphasizing the relative importance of sedentary behavior, alcohol consumption, and protein intake are beneficial for managing body weight status in adults. The specific risk thresholds for lifestyle factors observed in this study can help inform and guide future research and public health actions.

Smart farming based on IoT to predict conditions using machine learning

<span>Smart farming is a type of technology that utilizes the internet of things (IoT) to provide information on agricultural and environmental conditions as well as perform automation. Some of these ecological conditions can be used and analyzed in machine learning (ML) data management. This study focuses on utilizing ML algorithms to find the best prediction; typically used methods include linear regression, decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). In the application of smart farming, research on IoT and artificial intelligence (AI) is still uncommon since most IoT cannot make predictions like AI. Because basically, some IoT can't make predictions as AI does. In this Study, predictions were made by looking at the regression results in the form of root mean square error (RMSE) and absolute error. The results show a strong and weak correlation between features (positive or negative). The best prediction results are obtained by XGBoost when predicting temperature (RMSE 6.656 and absolute error 3.948) and (soil moisture 17.151 and absolute error 11.269). However, using different parameters (RMSE RF and absolute error DT) on RF and DT resulted in good and distinct results. Linear regression, on the other hand, produced unsatisfactory and poor result.</span>

Comparison of SEMG-Based Hand Gesture Classifiers

Machine learning techniques have shown success in classifying hand gestures. As the prevalence of prosthetic devices continues to rise, the adoption of non-invasive technologies, such as surface electromyography (sEMG), becomes paramount. This study systematically assesses the isolated influence of classification algorithms within hand gesture recognition (HGR) systems using sEMG data and dynamic time warping (DTW) based features. This approach effectively handles temporal variations in sEMG signals by leveraging DTW, ensuring input features are invariant to gesture speed. Six supervised learning classifiers were evaluated: the multilayer perceptron, support vector machine, logistic regression, linear discriminant analysis, k-nearest neighbors, and decision tree. Cross-validation was employed to fine-tune the segmentation hyperparameters, significantly improving results. To ensure reproducibility, the source code has been made available, the proposed system design has been detailed, and the evaluation protocols have been described. Our findings indicate that logistic regression outperformed other classifiers in this setup, achieving 95.2% accuracy in classifying six hand movements from ten healthy individuals, representing a 1.6% improvement over the best previously reported performance using the same publicly available dataset. Future research will assess the proposed HGR system’s generalization capability on larger datasets suitable for training more complex classifiers, including deep learning models.

Investigating students’ programming behaviors, interaction qualities and perceptions through prompt-based learning in ChatGPT

ChatGPT has proven to facilitate computer programming tasks through the strategic use of prompts, which effectively steer the interaction with the language model towards eliciting relevant information. However, the impact of specifically designed prompts on programming learning outcomes has not been rigorously examined through empirical research. This study adopted a quasi-experimental framework to investigate the differential effects of prompt-based learning (PbL) versus unprompted learning (UL) conditions on the programming behaviors, interaction qualities, and perceptions of college students. The study sample consisted of 30 college students who were randomly assigned to two groups. A mixed-methods approach was employed to gather multi-faceted data. Results revealed notable distinctions between the two learning conditions. First, the PbL group students frequently engaged in coding with Python and employed debugging strategies to verify their work, whereas their UL counterparts typically transferred Python code from PyCharm into ChatGPT and posed new questions within ChatGPT. Second, PbL participants were inclined to formulate more complex queries independently, prompted by the guiding questions, and consequently received more precise feedback from ChatGPT compared to the UL group. UL students tended to participate in more superficial-level interactions with ChatGPT, yet they also obtained accurate feedback. Third, there were noticeable differences in perception observed before and after the ChatGPT implementation, UL group reported a more favorable perception in the perceived ease of use in the pre-test, while the PbL group experienced an improvement in their mean scores for perceived usefulness, ease of use, behavioral intention to utilize, and a significant difference regarding the attitude towards utilizing ChatGPT. Specifically, the use of structured output and delimiters enhanced learners’ understanding of problem-solving steps and made learning more efficient with ChatGPT. Drawing on these outcomes, the study offers recommendations for the incorporation of ChatGPT into future instructional designs, highlighting the structured prompting benefits in enhancing programming learning experience.

Optimizing Cloud Data Storage: Evaluating File Formats for Efficient Data Warehousing

This paper presents a detailed analysis of three widely-used data storage formats—Parquet, Avro, and ORC— evaluating their performance across key metrics such as query execution, compression efficiency, data skipping, schema evolution, and throughput. Each format offers distinct advantages depending on the nature of the workload. Parquet is optimized for read-heavy analytical queries, providing excellent compression and efficient query performance through its columnar structure. Avro excels in write-heavy, real-time data streaming scenarios, where schema flexibility and backward compatibility are crucial. ORC balances the two, offering strong support for analytical and transactional workloads, especially in handling complex queries and nested data structures. This comparative study highlights the contexts in which each format performs best, providing valuable insights into the trade-offs associated with their use in cloud data warehouses and large-scale data processing environments.

Computational complexity of deep learning: fundamental limitations and empirical phenomena

Abstract This manuscript is the lecture notes of B. Barak’s course in the Les Houches ‘Statistical Physics and Machine Learning’ summer school in 2022. It surveys various proxies for computational hardness in random planted problems, from the low-degree likelihood ratio to statistical query complexity and the Franz–Parisi criterion, as well as the various relationships between those criteria. We also present a few aspects of the study of deep learning, from both a theoretical and empirical point of view.

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.

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.

The Comprehensive Investigation for Covid-19 Trend Prediction Through Machine Learning and Deep Learning

Corona Virus Disease (Covid-19) has surely been a challenging problem to solve for the past few years. Due to the diversity in the form of dataset, it is essential to obtain accurate predictive results of Covid-19 trends. This paper analyzes different artificial intelligence methods used in Covid-19 trend prediction, including several machine learning and deep learning methods. More specifically, this work investigates linear regression, random forest, and decision trees in terms of machine learning and delves into Artificial Neural Network (ANN) as well as Long Short-Term Memory (LSTM) for deep learning. By comparing various past works, the effectiveness of machine learning and deep learning methods is achieved by their hidden algorithms, such as the Multiple Linear Regression (MLR) model for linear regression analysis. Incorporation with other models or methods is applied in deep learning. For example, Ensemble Empirical Mode Decomposition (EEMD) is included in ANN structure to decrease the noises within the Covid-19 datasets. Furthermore, the paper also inquiries into potential improvement of some drawbacks in predictive results for Covid-19 trends by reviewing related works of expert system and transfer learning as well as domain adaptation. The machine learning and deep learning models could provide accurate predictive results as a reference for related organizations to consider or establish insightful policies.

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.

Distribution Characteristics of Trichiurus japonicus and Their Relationships with Environmental Factors in the East China Sea and South-Central Yellow Sea

The largehead hairtail (Trichiurus japonicus) is the most productive fish caught in China. In order to understand the seasonal distribution of T. japonicus in the East China Sea and the central and southern parts of the Yellow Sea, three species distribution models were used in this study, namely the random-forest model, K-nearest-neighbor algorithm, and gradient-ascending decision-tree model, based on the data of trawling surveys in the East China Sea and central and southern parts of the Yellow Sea from 2008 to 2009. Combined with a variance inflation factor and cross-check, a distribution model of T. japonicus was screened and constructed to analyze the influence of environmental factors on the distribution of T. japonicus in the East China Sea and central and southern parts of the Yellow Sea. The results showed that the random-forest model had the advantages of fitting effect and prediction ability among the three models. The analysis of this model showed that the water depth, bottom water temperature, and surface salinity had a great influence on the habitat distribution of T. japonicus. The relative resources of T. japonicus increased with the increase of bottom water temperature, reached the maximum at 23.8 °C, and first increased and then decreased with the increase of water depth and surface salinity, reaching the maximum when water depth is 72 m and surface salinity is 31.2%. This study also used the random-forest model to predict the spatial distribution of T. japonicus in the central and southern waters of the East China Sea and south-central Yellow Sea from 2008 to 2009, and the results showed that the predicted results were close to the actual situation. The research results can provide a reference for the exploitation and protection of T. japonicus resources in the East China Sea and the south-central Yellow Sea.

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.

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 &lt; 0.001, r = 0.36; p &lt; 0.001, r = 0.29; p &lt; 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.

Quantifying Tennis Players’ Performance and Analyzing Momentum Fluctuations Based on Weighted Logistic Regression Model

This paper proposes a dual-model approach to analyze tennis players’ performance and predict match outcomes, which combines a weighted logistic regression model and a decision tree model. By examining key factors such as scoring rate and serving status, the models calculate the scoring probabilities at different time points, quantifying the performance of players throughout the match. Furthermore, the paper constructs a decision tree model to predict shifts in the match situation based on momentum fluctuations. The research findings underscore the importance of momentum in determining match outcomes and provide valuable insights for training and match strategies. This dual-model approach offers a fresh perspective on the impact of momentum in tennis matches, allowing players to better understand and adapt to the dynamics of momentum fluctuations throughout a match. Additionally, the study suggests targeted training, simulated match conditions, psychological training, real-time data tracking, and physical preparation as strategies to enhance performance and adapt to momentum changes. This comprehensive approach provides players with valuable insights to optimize their performance and strategies in tennis matches.

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.

A Multi-Model Output Fusion Strategy Based on Various Machine Learning Techniques for Product Price Prediction

In the digital era, precise product price prediction becomes crucial for enhancing competitiveness in the online marketplace. This paper presents a hybrid model framework that enhances the accuracy of online product price predictions by integrating several machine learning algorithms, including Linear Regression, Decision Trees, and Gradient Boosting. The objective of this approach is to leverage the distinct advantages of each model to address their individual limitations and create a robust unified predictive model. This integration allows for improved handling of complex data relationships and diverse market dynamics that are typical in online sales environments. The results demonstrate that the hybrid model achieves superior prediction accuracy, as reflected in reduced Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics, and an exceptionally high R ² value compared to single-model approaches. These outcomes underscore the efficacy of combining multiple predictive models to enhance the precision of price forecasts in the highly competitive online marketplace. This model fusion strategy not only provides more accurate pricing predictions but also offers strategic insights into the optimal pricing strategies for businesses looking to enhance their market position.

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.

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.