Development Of Learning Model Research Articles

Development and validation of machine learning models for MASLD: based on multiple potential screening indicators

Development and validation of machine learning models for MASLD: based on multiple potential screening indicators

A deep learning tissue classifier based on differential Co-expression genes predicts the pregnancy outcomes of cattle.

Economic losses in cattle farms are frequently associated with failed pregnancies. Some studies found that the transcriptomic profiles of blood and endometrial tissues in cattle with varying pregnancy outcomes display discrepancies even before artificial insemination (AI) or embryo transfer (ET). In the study, 330 samples from seven distinct sources and two tissue types were integrated and divided into two groups based on the ability to establish and maintain pregnancy after AI or ET: P (pregnant) and NP (nonpregnant). By analyzing gene co-variation and employing machine learning algorithms, the objective was to identify genes that could predict pregnancy outcomes in cattle. Initially, within each tissue type, the top 100 differentially co-expressed genes (DCEG) were identified based on the analysis of changes in correlation coefficients and network topological structure. Subsequently, these genes were used in models trained by seven different machine learning algorithms. Overall, models trained on DCEGs exhibited superior predictive accuracy compared to those trained on an equivalent number of differential expression genes (DEGs). Among them, the deep learning models based on differential co-expression genes in blood and endometrial tissue achieved prediction accuracies of 91.7% and 82.6%, respectively. Finally, the importance of DCEGs was ranked using SHapley Additive exPlanations (SHAP) and enrichment analysis, identifying key signaling pathways that influence pregnancy. In summary, this study identified a set of genes potentially affecting pregnancy by analyzing the overall co-variation of gene connections between multiple sources. These key genes facilitated the development of interpretable machine learning models that accurately predict pregnancy outcomes in cattle.

An ensemble deep learning framework for energy demand forecasting using genetic algorithm-based feature selection.

Accurate energy demand forecasting is critical for efficient energy management and planning. Recent advancements in computing power and the availability of large datasets have fueled the development of machine learning models. However, selecting the most appropriate features to enhance prediction accuracy and robustness remains a key challenge. This study proposes an ensemble approach that integrates a genetic algorithm with multiple forecasting models to optimize feature selection. The genetic algorithm identifies the optimal subset of features from a dataset that includes historical energy consumption, weather variables, and temporal characteristics. These selected features are then used to train three base learners: Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU). The predictions from these models are combined using a stacking ensemble technique to generate the final forecast. To enhance model evaluation, we divided the dataset into weekday and weekend subsets, allowing for a more detailed analysis of energy consumption patterns. To ensure the reliability of our findings, we conducted ten simulations and applied the Wilcoxon Signed Rank Test to the results. The proposed model demonstrated exceptional precision, achieving a Root Mean Square Error (RMSE) of 130.6, a Mean Absolute Percentage Error (MAPE) of 0.38%, and a Mean Absolute Error (MAE) of 99.41 for weekday data. The model also maintained high accuracy for weekend predictions, with an RMSE of 137.41, a MAPE of 0.42%, and an MAE of 105.67. This research provides valuable insights for energy analysts and contributes to developing more sophisticated demand forecasting methods.

Development of an interpretable machine learning model based on CT radiomics for the prediction of post acute pancreatitis diabetes mellitus

Development of an interpretable machine learning model based on CT radiomics for the prediction of post acute pancreatitis diabetes mellitus

Development of an Active Play Learning Model to Promote Executive Functions for Elementary School Students

In this study, we aimed to develop an active play learning model to enhance cognitive thinking skills in elementary school students. We employed a qualitative research methodology, targeting health and physical education teachers from schools in Suphan Buri Province in Thailand. We collected data through individual surveys and in-depth interviews and performed data synthesis using documents and focus groups to guide the model’s development. The research findings revealed that teachers have a moderate understanding of cognitive thinking skills and the active play learning model. Although sports activities are regularly incorporated into lessons, they are not done so effectively enough to develop cognitive thinking skills. However, teachers recognize the importance of organizing activities to help students develop social skills through play. Therefore, we recommended additional training to enhance the active play learning model’s knowledge and understanding and ensure its effective implementation in teaching and learning. Based on data synthesis, we focused on three key areas: working memory skills, inhibitory control and reflective thinking skills, and cognitive flexibility skills. We utilized a variety of games and activities, such as rock-paper-scissors, imaginative block games, and treasure hunts using maps, to stimulate skill development in each area.

Product Feasibility of Developing a Learning Model for Counting in Elementary School Based on Motor Skills

The development of learning models needs to be done every time by taking into account the performance of students and technological developments. The purpose of this study is to describe the feasibility of product development of learning models for counting in elementary schools (SD) based on motor skills. The type of research is Research and Development using the ADDIE development model. The study population was fourth grade students in Karanganyar district, Central Java. Data collection through: limited trial, feasibility trial and hypothesis testing. The results of the study were limited tests obtained the number of scores obtained from teachers and students, namely 239 over the range 194.25≤x, so it can be concluded that the score is in the very feasible category; the feasibility test of the test results increased from 61.25 to 89.50; hypothesis testing obtained t count (19. 305) > t table (1.72913); experimental group hypothesis test t value (23.399) > t table (1.711) and control group t value (16.593) > t table (1.714), this shows that the learning model developed is feasible to be applied to mathematics learning. The conclusion of the research is that the development of a learning model for counting in elementary schools based on motor skills is categorized as feasible to be applied in learning mathematics.

XorSHAP: Privacy-Preserving Explainable AI for Decision Tree Models

Explainable AI (XAI) refers to the development of AI systems and machine learning models in a way that humans can understand, interpret and trust the predictions, decisions and outputs of these models. A common approach to explainability is feature importance, that is, determining which input features of the model have the most significant impact on the model prediction. Two major techniques for computing feature importance are LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). While very generic, these methods are computationally expensive even when the data is not encrypted. Applying them in the privacy-preserving setting when part or all of the input data is private is therefore a major computational challenge. In this paper, we present XorSHAP - the first practical data-oblivious algorithm for computing SHAP values for decision tree ensemble models. The algorithm is applicable in various privacy-preserving settings such as SMPC, FHE and differential privacy. Our algorithm has complexity O ( T M ~ D 2 D ) , where T is the number of decision trees in the ensemble, D is the depth of the decision trees and M ~ is the maximum of the number of features M and 2 D (the number of leaf nodes of a tree), and scales to real-world datasets. We implement the algorithm in the semi-honest Secure Multiparty Computation (SMPC) setting with full threshold using Inpher's Manticore framework. Our implementation simultaneously computes the SHAP values for 100 samples for an ensemble of T = 60 trees of depth D = 4 and M = 100 features in just 7.5 minutes, meaning that the SHAP values for a single prediction are computed in just 4.5 seconds for the same decision tree ensemble model. Additionally, it is parallelization-friendly, thus, enabling future work on massive hardware acceleration with GPUs.

Development and external validation of a machine learning model for brain injury in pediatric patients on extracorporeal membrane oxygenation

BackgroundPatients supported by extracorporeal membrane oxygenation (ECMO) are at a high risk of brain injury, contributing to significant morbidity and mortality. This study aimed to employ machine learning (ML) techniques to predict brain injury in pediatric patients ECMO and identify key variables for future research.MethodsData from pediatric patients undergoing ECMO were collected from the Chinese Society of Extracorporeal Life Support (CSECLS) registry database and local hospitals. Ten ML methods, including random forest, support vector machine, decision tree classifier, gradient boosting machine, extreme gradient boosting, light gradient boosting machine, Naive Bayes, neural networks, a generalized linear model, and AdaBoost, were employed to develop and validate the optimal predictive model based on accuracy and area under the curve (AUC). Patients were divided into retrospective cohort for model development and internal validation, and one cohort for external validation.ResultsA total of 1,633 patients supported by ECMO were included in the model development, of whom 181 experienced brain injury. In the external validation cohort, 30 of the 154 patients experienced brain injury. Fifteen features were selected for the model construction. Among the ML models tested, the random forest model achieved the best performance, with an AUC of 0.912 for internal validation and 0.807 for external validation.ConclusionThe Random Forest model based on machine learning demonstrates high accuracy and robustness in predicting brain injury in pediatric patients supported by ECMO, with strong generalization capabilities and promising clinical applicability.

Multi-site, multi-vendor development and validation of a deep learning model for liver stiffness prediction using abdominal biparametric MRI.

Chronic liver disease (CLD) is a substantial cause of morbidity and mortality worldwide. Liver stiffness, as measured by MR elastography (MRE), is well-accepted as a surrogate marker of liver fibrosis. To develop and validate deep learning (DL) models for predicting MRE-derived liver stiffness using routine clinical non-contrast abdominal T1-weighted (T1w) and T2-weighted (T2w) data from multiple institutions/system manufacturers in pediatric and adult patients. We identified pediatric and adult patients with known or suspected CLD from four institutions, who underwent clinical MRI with MRE from 2011 to 2022. We used T1w and T2w data to train DL models for liver stiffness classification. Patients were categorized into two groups for binary classification using liver stiffness thresholds (≥ 2.5 kPa, ≥ 3.0 kPa, ≥ 3.5 kPa, ≥ 4 kPa, or ≥ 5 kPa), reflecting various degrees of liver stiffening. We identified 4695 MRI examinations from 4295 patients (mean ± SD age, 47.6 ± 18.7 years; 428 (10.0%) pediatric; 2159 males [50.2%]). With a primary liver stiffness threshold of 3.0 kPa, our model correctly classified patients into no/minimal (< 3.0 kPa) vs moderate/severe (≥ 3.0 kPa) liver stiffness with AUROCs of 0.83 (95% CI: 0.82, 0.84) in our internal multi-site cross-validation (CV) experiment, 0.82 (95% CI: 0.80, 0.84) in our temporal hold-out validation experiment, and 0.79 (95% CI: 0.75, 0.81) in our external leave-one-site-out CV experiment. The developed model is publicly available ( https://github.com/almahdir1/Multi-channel-DeepLiverNet2.0.git ). Our DL models exhibited reasonable diagnostic performance for categorical classification of liver stiffness on a large diverse dataset using T1w and T2w MRI data. Question Can DL models accurately predict liver stiffness using routine clinical biparametric MRI in pediatric and adult patients with CLD? Findings DeepLiverNet2.0 used biparametric MRI data to classify liver stiffness, achieving AUROCs of 0.83, 0.82, and 0.79 for multi-site CV, hold-out validation, and external CV. Clinical relevance Our DeepLiverNet2.0 AI model can categorically classify the severity of liver stiffening using anatomic biparametric MR images in children and young adults. Model refinements and incorporation of clinical features may decrease the need for MRE.

Cardioattentionnet: advancing ECG beat characterization with a high-accuracy and portable deep learning model.

The risk of mortality associated with cardiac arrhythmias is considerable, and their diagnosis presents significant challenges, often resulting in misdiagnosis. This situation highlights the necessity for an automated, efficient, and real-time detection method aimed at enhancing diagnostic accuracy and improving patient outcomes. The present study is centered on the development of a portable deep learning model for the detection of arrhythmias via electrocardiogram (ECG) signals, referred to as CardioAttentionNet (CANet). CANet integrates Bi-directional Long Short-Term Memory (BiLSTM) networks, Multi-head Attention mechanisms, and Depthwise Separable Convolution, thereby facilitating its application in portable devices for early diagnosis. The architecture of CANet allows for effective processing of extended ECG patterns and detailed feature extraction without a substantial increase in model size. Empirical results indicate that CANet outperformed traditional models in terms of predictive performance and stability, as confirmed by comprehensive cross-validation. The model demonstrated exceptional capabilities in detecting cardiac arrhythmias, surpassing existing models in both cross-validation and external testing scenarios. Specifically, CANet achieved high accuracy in classifying various arrhythmic events, with the following accuracies reported for different categories: Normal (97.37 ± 0.30%), Supraventricular (98.09 ± 0.25%), Ventricular (92.92 ± 0.09%), Atrial Fibrillation (99.07 ± 0.13%), and Unclassified arrhythmias (99.68 ± 0.06%). In external evaluations, CANet attained an average accuracy of 94.41%, with the area under the curve (AUC) for each category exceeding 99%, thereby demonstrating its substantial clinical applicability and significant advancements over traditional models. The deep learning model proposed in this study has the potential to enhance the accuracy of early diagnosis for various types of arrhythmias. Looking ahead, this technology is anticipated to provide improved medical services for patients with heart disease through continuous, non-invasive monitoring and timely intervention.

Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation.

Reliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with the highly non-linear causal relationships between physicochemical and biological properties and conventional molecular featurization schemes, complicates the development of robust molecular machine learning models. Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks. Yet, existing molecular SSL methods largely overlook chemical knowledge, including molecular structure similarity, scaffold composition, and the context-dependent aspects of molecular properties when operating over the chemical space. They also struggle to learn the subtle variations in structure-activity relationship. This paper introduces a multi-channel pre-training framework that learns robust and generalizable chemical knowledge. It leverages the structural hierarchy within the molecule, embeds them through distinct pre-training tasks across channels, and aggregates channel information in a task-specific manner during fine-tuning. Our approach demonstrates competitive performance across various molecular property benchmarks and offers strong advantages in particularly challenging yet ubiquitous scenarios like activity cliffs.

Development and External Validation of a Machine Learning Model to Predict Restriction from Spirometry.

Though European Respiratory Society and American Thoracic Society (ERS/ATS) guidelines for pulmonary function test (PFT) interpretation recommend the use of the forced vital capacity (FVC) lower limit of normal (LLN) to exclude restriction, recent data suggest that the negative predictive value (NPV) of the FVC LLN is lower than has been accepted, particularly among non-Hispanic Black patients. Using a machine learning (ML) model-rather than the FVC LLN-to exclude restriction may improve the accuracy and equity of PFT interpretation. We sought to develop and externally validate a ML model to predict restriction from spirometry and to assess the potential impact of this model on PFT interpretation. We included PFTs with both static and dynamic lung volume measurements for patients between 18 and 80 years of age tested at pulmonary diagnostic labs within two health systems. We used PFTs from one health system to train logistic regression, random forest, and boosted tree models to predict restriction using demographic, anthropometric, and spirometric data. We used PFTs from the second health system to externally validate these models. The primary measure of model performance was the NPV. Model equity was assessed by comparing the NPV among non-Hispanic Black and non-Hispanic White patients. A total of 42 462 PFTs were used for model development and 24 524 for external validation. The prevalence of restriction was 29.8% in the development dataset and 39.6% in the validation dataset. Performance was similar across the three ML models with the best performance seen with the random forest model. The overall NPV of the random forest model (88.3%, 95% confidence interval [CI] 87.8% to 88.9%) was significantly greater than that of the FVC LLN (72.7%, 95% CI 72.1% to 73.3%). The NPV of the random forest model was greater than that of the FVC LLN among both non-Hispanic Black (74.6% [95% CI 72.5% to 76.6%] versus 49.5% [95% CI 47.8% to 51.2%]) and non-Hispanic White (90.9% [95% CI 90.3% to 91.5%] versus 79.6% [95% CI 78.9% to 80.3%]) patients. Using a ML model to exclude restriction from spirometry improves both the accuracy and equity of PFT interpretation.

Development of transformative reflexive empowering character building learning model: a case study of Pancasila student profile at elementary schools

Introduction. The relevance of this research lies in addressing the lack of clear, practical standards for character education in elementary schools in Indonesia, which remains largely theoretical. The purpose of this article is to develop a learning model intended as a standard and guideline for teachers in the teaching and learning process of Pancasila student character profiles at the elementary school level. Study participant and methods. The research utilized the R&amp;D Borg and Gall method, which involved various stages from research and information collection to dissemination and implementation. The validation process included consultations with experts in student character teaching strategies to ensure the model's validity, design, and contextualization. The trial involved 10 teachers and 100 elementary school students from grades 4, 5, 6 across five different elementary schools in Indonesia. The results. The results of this study indicate that the Transformative Reflexive Empowering Character Building (TRECB) Model garnered motivation and interest from 93.3% to 96.6% of students, and there was a significant improvement in students' understanding of the Pancasila student profile. With an n-gain value of 0.75, the effectiveness of the TRECB model in enhancing students' understanding and application of the Pancasila student profile falls into the high category. Statistical observations also reveal a significant difference in the Pancasila Student Profile among students before and after the integration of the TRECB Model into their educational experience. Conclusion. The developed learning model provides a standard and guideline for teachers in character education at the elementary school level, addressing the lack of clear standards and implementation in character education in Indonesian elementary schools. The model, refined through expert consultations and trials, offers an effective approach to teaching and learning character education, paving the way for its implementation in elementary schools and potentially higher levels of education in the future.

Building Bright Minds: A Comprehensive Study on Developmentally Appropriate Practices for Enhancing Elementary Students' Mental Health

Introduction: Quality learning is essential to achieve national education goals. However, current practices often neglect students’ mental health needs, and focus more on academic achievement.Objective: The purpose of this study was to analyze the need to develop a Developmentally Appropriate Practices (DAP) learning model that specifically targets the mental health of elementary school students.Method: This study used a qualitative case study design approach, involving 30 teachers and 3 mental health professionals from Surakarta city. Data were collected through in-depth interviews with educators and mental health professionals, as well as classroom observations. Data were analyzed thematically to identify relevant patterns and themes related to the development of the DAP learning model. Interview guides and observation sheets were used as research instruments.Results: The findings revealed three main problems: 1) lack of attention to mental health aspects in the curriculum and learning practices, with an exclusive focus on academic performance; 2) lack of trained personnel to address mental health needs in schools; and 3) students face challenges in adapting to academic and social demands, which can lead to stress and mental health problems. The DAP learning model can improve the mental health of elementary school students by integrating emotional, social, and academic aspects, thereby fostering a supportive environment that increases students' sense of connectedness and confidence in learning.Conclusion: The development of the Developmentally Appropriate Practices (DAP) learning model is essential to address the mental health needs of elementary school students. By incorporating emotional and social dimensions into educational practices

Development of Robust Machine Learning Models for Predicting Flexural Strengths of Fiber-Reinforced Polymeric Composites

Development of Robust Machine Learning Models for Predicting Flexural Strengths of Fiber-Reinforced Polymeric Composites

Development and validation of an interpretable machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients.

Left atrial thrombus or spontaneous echo contrast (LAT/SEC) are widely recognized as significant contributors to cardiogenic embolism in non-valvular atrial fibrillation (NVAF). This study aimed to construct and validate an interpretable predictive model of LAT/SEC risk in NVAF patients using machine learning (ML) methods. Electronic medical records (EMR) data of consecutive NVAF patients scheduled for catheter ablation at the First Hospital of Jilin University from October 1, 2022, to February 1, 2024, were analyzed. A retrospective study of 1,222 NVAF patients was conducted. Nine ML algorithms combined with demographic, clinical, and laboratory data were applied to develop prediction models for LAT/SEC in NVAF patients. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. Multiple ML classification models were integrated to identify the optimal model, and Shapley Additive exPlanations (SHAP) interpretation was utilized for personalized risk assessment. Diagnostic performances of the optimal model and the CHA2DS2-VASc scoring system for predicting LAT/SEC risk in NVAF were compared. Among 1,078 patients included, the incidence of LAT/SEC was 10.02%. Six independent predictors, including age, non-paroxysmal AF, diabetes, ischemic stroke or thromboembolism (IS/TE), hyperuricemia, and left atrial diameter (LAD), were identified as the most valuable features. The logistic classification model exhibited the best performance with an area under the receiver operating characteristic curve (AUC) of 0.850, accuracy of 0.812, sensitivity of 0.818, and specificity of 0.780 in the test set. SHAP analysis revealed the contribution of explanatory variables to the model and their relationship with LAT/SEC occurrence. The logistic regression model significantly outperformed the CHA2DS2-VASc scoring system, with AUCs of 0.831 and 0.650, respectively (Z = 7.175, P < 0.001). ML proves to be a reliable tool for predicting LAT/SEC risk in NVAF patients. The constructed logistic regression model, along with SHAP interpretation, may serve as a clinically useful tool for identifying high-risk NVAF patients. This enables targeted diagnostic evaluations and the development of personalized treatment strategies based on the findings.

An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting

Non-alcoholic fatty liver disease (NAFLD) is a chronic medical ailment characterized by accumulation of excessive fat in the liver of non-alcoholic patients. In absence of any early visible indications, application of machine learning based predictive techniques for early prediction of NAFLD are quite beneficial. The objective of this paper is to present a complete framework for guided development of varied predictive machine learning models and predict NAFLD disease with high accuracy. The framework employs step–by-step data quality enhancement to medical data such as cleaning, normalization, data upscaling using SMOTE (for handling class imbalances) and correlation analysis-based feature selection to predict NAFLD with high accuracy using only clinically recorded identifiers. Comprehensive comparative analysis of prediction results of seven machine learning predictive models is done using unprocessed as well as quality enhanced data. As per the observed results, XGBoost, random forest and neural network machine learning models reported significantly higher accuracies with improved ‘AUC’ and ‘ROC’ values using preprocessed data in contrast to unprocessed data. The prediction results are also assessed on various quality metrics such as ‘accuracy’, ‘f1-score’, ‘precision’, and ‘recall’ significantly support the need for presented methodologies for qualitative NAFLD prediction modelling.

Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression.

Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and compare it with established models. Materials and Methods This retrospective study included 1607 MRI scans of 1143 male patients (median age, 64 years; IQR, 59-68 years) undergoing MRI for suspicion of clinically significant PCa (csPCa) (International Society of Urological Pathology grade > 1) between January 2012 and May 2022 who were negative for csPCa at baseline MRI. A DL model was developed using baseline MRI and clinical parameters (age, prostate-specific antigen [PSA] level, PSA density, and prostate volume) to predict the time to PCa progression (defined as csPCa diagnosis at follow-up). Internal and external testing was performed. The model's ability to predict progression to csPCa was assessed by Cox regression analyses. Predictive performance of the DL model up to 5 years after baseline MRI in comparison with the European Randomized Study of Screening for Prostate Cancer (ERSPC) future-risk calculator, Prostate Cancer Prevention Trial (PCPT) risk calculator, and Prostate Imaging Reporting and Data System (PI-RADS) was assessed using the Harrell C-index. Optimized follow-up intervals were derived from Kaplan-Meier curves. Results DL scores predicted csPCa progression (internal cohort: hazard ratio [HR], 1.97 [95% CI: 1.61, 2.41; P < .001]; external cohort: HR, 1.32 [95% CI: 1.14, 1.55; P < .001]). The model identified a subgroup of patients (approximately 20%) with risks for csPCa of 3% or less, 8% or less, and 18% or less after 1-, 2-, and 4-year follow-up, respectively. DL scores had a C-index of 0.68 (95% CI: 0.63, 0.74) at internal testing and 0.56 (95% CI: 0.51, 0.61) at external testing, outperforming ERSPC and PCPT (both P < .001) at internal testing. Conclusion The DL model accurately predicted PCa progression and provided improved risk estimations, demonstrating its ability to aid in personalized follow-up for low-risk PCa. Keywords: MRI, Prostate Cancer, Deep Learning Supplemental material is available for this article. ©RSNA, 2025.

Comments on “Artificial intelligence aided pharmaceutical engineering: Development of hybrid machine learning models for prediction of nanomedicine solubility in supercritical solvent”

Comments on “Artificial intelligence aided pharmaceutical engineering: Development of hybrid machine learning models for prediction of nanomedicine solubility in supercritical solvent”

Relationship analysis between deterioration of switch support and railway passenger comfort using machine learning

Passenger comfort is one of the critical factors in the railway system. Passenger comfort plays an important role in the success and effectiveness of railway transportation systems in terms of passenger satisfaction, health and well-being, economy, competition capability, and safety. For the railway infrastructure, one of the important components is a switch. Switches in the railway system have the function of guiding rolling stocks or trains to the preferred directions and tracks. However, switches are the components of the railway infrastructure that can negatively affect railway passenger comfort due to their geometry and the functions that are used to change the direction of rolling stocks. This study aims to study the relationship between the deterioration of railway switch support and railway passenger comfort by using machine learning. The data used in the study are axle box accelerations that are numerically simulated from verified multi-body simulation models. The machine learning technique used in the study is the convolutional neural network. The indicator used to evaluate the machine learning model’s performance is the accuracy. From the machine learning model development and training, the accuracy of the machine learning model is higher than 80% which is satisfied. Railway operators can benefit from the study’s findings by applying the developed machine learning model to collect data to evaluate the deterioration of railway switch support and the effect on railway passenger comfort.