Logistic Regression Algorithm Research Articles

Automated classification of online reviews of otolaryngologists.

The study aimed to extract online comments of otolaryngologists in the 20 most populated cities in the United States from healthgrades.com, develop and validate a natural language processing (NLP) logistic regression algorithm for automated text classification of reviews into 10 categories, and compare 1- and 5-star reviews in directly-physician-related and non-physician-related categories. 1977 1-star and 12,682 5-star reviews were collected. The primary investigator manually categorized a training dataset of 324 1-star and 909 5-star reviews, while a validation subset of 100 5-star and 50 1-star reviews underwent dual manual categorization. Using scikit-learn, an NLP algorithm was trained and validated on the subsets, with F1 scores evaluating text classification accuracy against manual categorization. The algorithm was then applied to the entire dataset with comparison of review categorization according to 1- and 5-star reviews. F1 scores for NLP validation ranged between 0.71 and 0.97. Significant associations emerged between 1-star reviews and treatment plan, accessibility, wait time, office scheduling, billing, and facilities. 5-star reviews were associated with surgery/procedure, bedside manner, and staff/mid-levels. The study successfully validated an NLP text classification system for categorizing online physician reviews. Positive reviews were found to have an association with directly-physician related context. 1-star reviews were related to treatment plan, accessibility, wait time, office scheduling, billing, and facilities. This method of text classification effectively discerned the nuances of human-written text, providing valuable insights into online healthcare feedback that is scalable.Level of evidence: Level 3.

Using advanced machine learning algorithms to predict academic major completion: A cross-sectional study

BackgroundExisting prediction methods for academic majors based on personality traits have notable gaps, including limited model complexity and generalizability.The current study aimed to utilize advanced Machine Learning (ML) algorithms with smoothing functions to predict academic majors completed based on personality subscales. MethodsWe used reports from 59,413 individuals to perform the current study. All advanced algorithms implemented in this article were based on R software (version 4.1.3, R Core Team, 2021). All model parameters were optimized based on resampling and cross-validation (CV). In addition, pseudo-R2 as a robust metric has been used to compare the performance of models, which, unlike most studies, considers the quality of model-predicted probabilities. ResultThe results indicated that advanced ML models' performance on training and test data was superior to logistic regression. Pseudo-R2 and AUC results showed that advanced models such as kNN, GBE, and RF had the highest scores based on test data compared to other models. The pseudo-R2 values for the models used in this study varied across the test dataset; the lowest value belonged to the logistic regression algorithm at .022, and the highest value was recorded for the kNN algorithm at .099. The agreeableness subscale is the most influential component in predicting the completion of university education, followed by conscientiousness and emotional stability. ConclusionThe potential of advanced methods to enhance the accuracy and validity of predictions is a promising development in our field. Their performance, particularly in handling large data sets with complex patterns, is a reason for optimism about the future of research in this area.

Machine learning-driven prediction of EBNA1 inhibitors against Epstein–Barr virus in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC), particularly prevalent in regions such as Malaysia, is a significant health concern often linked to Epstein-Barr virus (EBV) infection. The EBV nuclear antigen 1 (EBNA1), crucial for EBV survival and NPC tumorigenicity, has emerged as a potential therapeutic target for EBV-positive NPC. In this study, we utilized quantitative structure-activity relationship (QSAR) models to predict potential inhibitors of EBNA1. These models were developed based on the molecular fingerprints of known EBNA1 inhibitors, using both classification and regression approaches. Our QSAR classification models demonstrated consistently high precision, recall, F1 score, and accuracy scores across the training set. The top-performing models, constructed using logistic regression algorithms, achieved perfect precision scores of 1.000 in the test set evaluation. These models’ recall, F1 score, and accuracy scores were 0.571, 0.727, and 0.667, respectively. On the other hand, the best-performing model among the regression models was built using the sequential minimal optimization regression algorithm, achieving a correlation coefficient of 0.703. The mean absolute error and root mean square error of this QSAR regression model were 0.173 and 0.217, respectively, whereas the relative absolute error was 0.689. We screened the enamine advanced compound library using this regression model to predict compounds with potential EBNA1 inhibitory effects. This led to the identification of the top 10 compounds with the most promising predicted EBNA1 inhibitory properties.

Predicting nodal response to neoadjuvant treatment in breast cancer with core biopsy biomarkers of tumor microenvironment using data mining.

To generate a model for predicting nodal response to neoadjuvant systemic treatment (NAST) in biopsy-proven node-positive breast cancer patients (cN+) that incorporates tumor microenvironment (TME) characteristics and could be used for planning the axillary surgical staging procedure. Clinical and pathologic features were retrospectively collected for 437 patients. Core biopsy (CB) samples were reviewed for stromal content and tumor-infiltrating lymphocytes (TIL). Orange Datamining Toolbox was used for model generation and assessment. 151/437 (34.6%) patients achieved nodal pCR (ypN0). The following 5 variables were included in the prediction model: ER, Her-2, grade, stroma content and TILs. After stratified tenfold cross-validation, the logistic regression algorithm achieved and area under the ROC curve (AUC) of 0.86 and F1 score of 0.72. Nomogram was used for visualization. We developed a clinical tool to predict nodal pCR for cN+ patients after NAST that includes biomarkers of TME and achieves an AUC of 0.86 after tenfold cross-validation.

Predicting Sleep Disorders: Leveraging Sleep Health and Lifestyle Data with Dipper Throated Optimization Algorithm for Feature Selection and Logistic Regression for Classification

Predicting Sleep Disorders: Leveraging Sleep Health and Lifestyle Data with Dipper Throated Optimization Algorithm for Feature Selection and Logistic Regression for Classification

Determining the influence of data on working with video materials on the accuracy of student success prediction models

The object of this study is models for predicting students’ success, constructed on the basis of machine learning methods. The paper reports results of research into the problem of improving their accuracy by expanding the data set for training the specified models. The most available are data on student actions, which are automatically collected by learning management systems. Entering additional information about students’ work increases time and resources but allows the improvement of the accuracy of the models. In the study, information about students’ work with video materials, particularly the number and duration of views, was entered into the original data set. To automate the collection of this data, the plugin for the Moodle system has been developed, which stores information about user’s actions with the video player and the duration of watching video materials in the database. Model training was carried out using Naive Bayes (NB), logistic regression (LR), random forest (RF), and neural networks (NN) algorithms with and without video data. For the models using video viewing data, accuracy increased by 10 %, balanced accuracy by 15 %, and overall performance, expressed as area under the curve (AUC), increased by 14 %. The highest prediction accuracy, with a difference of 1.8 %, was obtained by models built using RF algorithms – 87.1 % and NN – 85.3 %. At the same time, the accuracy of the models obtained by the NB and LR algorithms was 70.7 % and 76.5 %. The increase in accuracy for them was 2.3 % and 8.1 %, respectively. Analysis of calculations confirms the assumption that students’ work with educational video materials is correlated with their success. The results make it possible to find a reasonable compromise between model development costs and its accuracy at the stage of data preparation for model training.

DPDCD: The Intelligent Prediction Strategy for the Course Development of Diabetic Kidney Disease

Diabetic nephropathy (DKD) is a severe diabetes complication and a leading cause of mortality. While urine microalbuminuria and eGFR are common markers, some advanced cases may exhibit normal values. The progression from microalbuminuria to overt proteinuria in DKD is gradual and requires comprehensive physiological tests for diagnosis. This study employs deep reinforcement learning to predict DKD progression based on patients’ physiological data, aiming to assist clinical diagnosis and treatment. A multivariate logistic regression algorithm is used to describe the DKD prediction probability. An optimized DKD progression prediction algorithm (DPDCD) based on deep reinforcement learning determines the best model coefficients, enabling accurate prediction of DKD progression using routine clinical data. Experimental results demonstrate that DPDCD outperforms other algorithms in predicting DKD progression, providing valuable support for clinicians.

Development and Validation of Machine Learning Models for Predicting Tumor Progression in OSCC.

Development of a prediction model using machine learning (ML) method for tumor progression in oral squamous cell carcinoma (OSCC) patients would provide risk estimation for individual patient outcomes. This predictive modeling study was conducted of 1163 patients with OSCC from Hospital of Stomatology, SYSU and SYSU Cancer Center from March 2009 to October 2021. Clinical, pathological, and hematological features of the patients were collected. Six ML algorithms were explored, and model performance was assessed by accuracy, sensitivity, specificity, f1 score, and AUC. SHAP values were used to identify the variables with the greatest contribution to the model. Among the 1163 patients (mean [SD] age, 55.36 [12.91] years), 563 are from development cohort and 600 are from validation cohort. The Logistic Regression algorithm outperformed all other models, with a sensitivity of 94.7% (68.2%), a specificity of 55.3% (63.7%), and the AUC of 0.76 ± 0.09 (0.723) in the development (validation) cohort. The most predictive feature was neutrophil count. This study demonstrated ML models can improve clinical prediction of oral squamous cell carcinoma progression through basic information of patients. These tools could be used to provide individual risk estimation and may help direct intervention.

Construction and validation of a line chart for gestational diabetes mellitus based on clinical indicators

BackgroundGestational diabetes mellitus (GDM) is a common complication of mid–to-late pregnancy. Here, we constructed a predictive model for GDM based on a combination of clinical characteristics and relevant serum markers.MethodsData from full-term singleton vaginal deliveries from January 2022 to January 2023 were retrospectively collected from the obstetrics department. The data collected were segregated and assigned to training, validation, and external test sets. Maternal demographic characteristics, living and working habits, and haematological indicators, such as liver function and lipids were collected using a questionnaire designed for the study. The “rms” package in R was used to explore GDM-associated factors through stepwise regression at P < 0.05. A predictive model was developed based on the results of multifactorial logistic regression analysis. We then evaluated the differentiation of the column-line graphical model and performed internal and external validation. To assess the accuracy of the bar graphical model, we plotted calibration and decision curves.ResultsData from 265 pregnant women were included in the training and internal validation sets, and data from 113 pregnant women were included in the external validation set. The logistic regression algorithm screened 8 indicators as predictors. A prediction model was constructed with ALT, TBA, TC, and TG levels while considering whether GDM affects appetite, the husband– wife relationship, family history, and parental relationships as predictors. The Hosmer–Lemeshow goodness-of-fit test revealed that the chi-square values for the modelling, internal validation, and external validation groups (χ2 = 5.964, 3.249, and 12.182, respectively) were all P > 0.05. The ROC curve AUCs for the three groups were 0.93 (95% CI: 0.89–0.97), 0.72 (95% CI: 0.62–0.81), and 0.68 (95% CI: 0.53–0.83), respectively.ConclusionIn this study, a GDM prediction model was constructed to achieve high performance in GDM risk prediction based on routine obstetric tests and information.

Improvement for deep learning for suicide and depression identification with unsupervised label correction

Abstract. Our study try to address the challenge of accurately identifying depression and suicidal ideation on social media platforms by introducing an enhanced novel methodology for unsupervised feature selection and label correction. Utilizing advanced word embedding models like BERT and the Universal Sentence Encoder, we transform textual content into dense numerical vectors that capture the nuanced emotional context of online discussions. Our approach enhances these embeddings with a deep neural network (DNN) to extract distinctive features, reducing the dimensionality of the data through Principal Component Analysis (PCA). For label correction, we employ clustering techniques including OPTICS, K-medoids, and hierarchical clustering, which are robust against noisy data points. We then train classifiers using CNN, DNN, logistic regression, and random forest algorithms, evaluated with metrics such as accuracy, precision, recall, F1 score, and AUC. This methodology improves the accuracy of classifying depressive and suicidal sentiments to some extent, assisting to utilize the vast data available on social media to advance mental health diagnostics and interventions.

A predictive model for secondary central nervous system infection after craniotomy based on machine learning

To analyze the risk factors of secondary Central nervous system infections (CNSIs) after craniotomy, and to establish an individualized predictive model for CNSIs risk. The independent risk factors were screened by univariate and multivariate logistic regression analysis. Logistic regression, naive bayes, random forest, light GBM and adaboost algorithms were used to establish predictive models for secondary CNSIs after craniotomy. The predictive model based on the Adaboost algorithm demonstrated superior prediction performance compared to the other four models. Under 5-fold cross validation, the accuracy was 0.80, the precision was 0.69, the recall was 0.85, the F1-score was 0.76, the area under the ROC curve was 0.897,and the average precision was 0.880. The top 5 variables of importance in Adaboost model were operation time, indwelling time of lumbar drainage tube, indwelling lumbar drainage tube during operation, indwelling epidural drainage tube during operation, and GCS score. In addition, Adaboost model with the best prediction performance was used for clinical verification, and the prediction results were compared with the actual occurrence of CNSIs after surgery. The results showed that the accuracy of Adaboost model in predicting CNSIs was 60%, the accuracy of Adaboost model in predicting non-CNSIS was 92%, and the overall prediction accuracy was 76%.

Landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantation

Rejection seriously affects the success of kidney transplantations. However, the molecular mechanisms underlying this rejection remain unclear. The GSE21374 and GSE36059 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Next, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to infer the proportions of 22 immune cells. Moreover, infiltrating immune cell-related genes were identified using weighted gene co-expression network analysis (WGCNA), and enrichment analysis was conducted to observe their biological functions. Extreme Gradient Boosting (XGBoost) and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression algorithms were used to screen hub genes. Quantitative real-time PCR was conducted to verify the number of immune cells and hub gene expression levels. The rejection and non-rejection groups showed significantly different distributions (P < 0.05) of eight immune cells (B cell memory, Plasma cells, mast cells, follicular helper T cells, T CD8 cells, Macrophages M1, T Cells CD4 memory activated, and gamma delta T cells). Subsequently, CD8A, CRTAM, GBP2, WARS, and VAMP5 were screened as hub genes using the XGBoost and LASSO algorithms and could be used as diagnostic biomarkers. Finally, differential analysis and quantitative real-time PCR suggested that CD8A, CRTAM, GBP2, WARS, and VAMP5 were upregulated in rejection samples compared to non-rejection samples. The present study identified five key infiltrating immune cell-related genes (CD8A, CRTAM, GBP2,WARS, and VAMP5) involved in kidney transplant rejection, which may explain the molecular mechanism of rejection in kidney transplantation development.

Tennis Game Swing Prediction Model Based on ID3 Algorithm and Logistic Regression Model

Tennis, as one of the most popular sports worldwide, includes exciting matches between players. In some matches, score advantage can swing constantly from one side to another. These score swings may impact players’ performance and the result. To accurately predict the swings during a match between players, the article aims at building a score swing prediction model based on ID3 algorithm and logistic regression algorithm. The article will focus on analyzing tennis players’ behaviors and performance during a match. The preparation is to form a decision tree to evaluate diverse events’ impact on players’ mental strength, using ID3 algorithm that can analyze desired indicators with relevant data. By using the results from the decision tree evaluation, indicators with higher priority will be analyzed by the logistic regression model. The following Omnibus test and Hosmer-Lemeshow tests will estimate significance value and prediction accuracy. The results can show the prediction of scores swings in a match and indicate the factors that impact the match more. The study can provide tennis players with theoretical advice on their training activities, which may improve the ability of reversing the match and being consistent under pressure effectively.

MRI-based radiomics model for predicting endometrial cancer with high tumor mutation burden.

To evaluate the performance of MRI-based radiomics in predicting endometrial cancer (EC) with a high tumor mutation burden (TMB-H). A total of 122 patients with pathologically confirmed EC (40 TMB-H, 82 non-TMB-H) were included in this retrospective study. Patients were randomly divided into training and testing cohorts in a ratio of 7:3. Radiomics features were extracted from sagittal T2-weighted images and contrast-enhanced T1-weighted images. Then, the logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms were used to construct radiomics models. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the diagnostic performance of each model, and decision curve analysis was used to determine their clinical application value. Four radiomics features were selected to build the radiomics models. The three models had similar performance, achieving 0.771 (LR), 0.892 (RF), and 0.738 (SVM) in the training cohort, and 0.787 (LR), 0.798 (RF), and 0.777 (SVM) in the testing cohort. The decision curve demonstrated the good clinical application value of the LR model. The MRI-based radiomics models demonstrated moderate predictive ability for TMB-H EC and thus may be a tool for preoperative, noninvasive prediction of TMB-H EC.

Machine learning-based prediction of mortality in acute myocardial infarction with cardiogenic shock.

In the ICU, patients with acute myocardial infarction and cardiogenic shock (AMI-CS) often face high mortality rates, making timely and precise mortality risk prediction crucial for clinical decision-making. Despite existing models, machine learning algorithms hold the potential for improved predictive accuracy. In this study, a predictive model was developed using the MIMIC-IV database, with external validation performed on the eICU-CRD database. We included ICU patients diagnosed with AMI-CS. Feature selection was conducted using the Boruta algorithm, followed by the construction and comparison of four machine learning models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Gaussian Naive Bayes (GNB). Model performance was evaluated based on metrics such as AUC (Area Under the Curve), accuracy, sensitivity, specificity, and so on. The SHAP method was employed to visualize and interpret the importance of model features. Finally, we constructed an online prediction model and conducted external validation in the eICU-CRD database. In this study, a total of 570 and 391 patients with AMI-CS were included from the MIMIC-IV and eICU-CRD databases, respectively. Among all machine learning algorithms evaluated, LR exhibited the best performance with a validation set AUC of 0.841(XGBoost: 0.835, AdaBoost: 0.839, GNB: 0.826). The model incorporated five variables: prothrombin time, blood urea nitrogen, age, beta-blockers and Angiotensin-Converting Enzyme Inhibitors or Angiotensin II Receptor Blockers. SHAP plots are employed to visualize the importance of model features and to interpret the results. An online prediction tool was developed, externally validated with the eICU-CRD database, achieving an AUC of 0.755. Employing the LR algorithm, we developed a predictive model for assessing the mortality risk among AMI-CS patients in the ICU setting. Through model predictions, this facilitates early detection of high-risk individuals, ensures judicious allocation of healthcare resources.

Early Detection of Congenital Heart Diseases among Infants Using Artificial Neural Network Algorithm

Congenital Heart Disease (CHD) detection has continued to witness a consistent increase in research attention. CHD diseases are vast and also span across diverse demographics, without sparing pregnant women, unborn babies, or newly born babies. The aim of this study is to develop a detection model capable of detecting heart disease among infants with high accuracy and to also suggest solutions to manage it. To achieve this, the CHD types were identified to develop a data model that considered infants. The data model was processed through imputation, feature selection, and transformation using the imputation method and Principal Component Analysis (PCA). After the data processing stage, Artificial Neural Network (ANN) algorithm is adopted and trained with the data to generate the model for the detection of the CHD. Comparative analysis was used to evaluate the performance of the models in comparison with other models adopted in other works, considering metrics that defined the success of the detection models. The results showed that the ANN has the best detection outcome with an accuracy of 97.44%. Although the use of Logistic Regression algorithm attained a high level of performance with an accuracy of 95.00%, but it still falls below the proposed ANN algorithm. Another highly performing algorithm is the use of Extreme Gradient Boosting (XGB) which achieved an accuracy of 92.00%.

The PANoptosis-related hippocampal molecular subtypes and key biomarkers in Alzheimer’s disease patients

Alzheimer’s Disease (AD) is a neurodegenerative disorder, and various molecules associated with PANoptosis are involved in neuroinflammation and neurodegenerative diseases. This work aims to identify key genes, and characterize PANoptosis-related molecular subtypes in AD. Moreover, we establish a scoring system for distinguishing PANoptosis molecular subtypes and constructing diagnostic models for AD differentiation. A total of 5 hippocampal datasets were obtained from the Gene Expression Omnibus (GEO) database. In total, 1324 protein-encoding genes associated with PANoptosis (1313 apoptosis genes, 11 necroptosis genes, and 31 pyroptosis genes) were extracted from the GeneCards database. The Limma package was used to identify differentially expressed genes. Weighted Gene Co-Expression Network Analysis (WGCNA) was conducted to identify gene modules significantly associated with AD. The ConsensusClusterPlus algorithm was used to identify AD subtypes. Gene Set Variation Analysis (GSVA) was used to assess functional and pathway differences among the subtypes. The Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithms were used to select the three PANoptosis-related Key AD genes (PKADg). A scoring model was constructed based on the Boruta algorithm. PANoptosis diagnostic models were developed using the RF, SVM-RFE, and Logistic Regression (LR) algorithms. The ROC curves were used to assess the model performance. A total of 48 important genes were identified by intersecting 725 differentially expressed genes and 2127 highly correlated module genes from WGCNA with 1324 protein-encoding genes related to PANoptosis. Machine learning algorithms identified 3 key AD genes related to PANoptosis, including ANGPT1, STEAP3, and TNFRSF11B. These genes had strong discriminatory capacities among samples, with Receiver Operating Characteristic Curve (ROC) analysis indicating Area Under the Curve (AUC) values of 0.839, 0.8, and 0.868, respectively. Using the 48 important genes, the ConsensusClusterPlus algorithm identified 2 PANoptosis subtypes among AD patients, i.e., apoptosis subtype and mild subtype. Apoptosis subtype patients displayed evident cellular apoptosis and severe functionality damage in the hippocampal tissue. Meanwhile, mild subtype patients showed milder functionality damage. These two subtypes had significant differences in apoptosis and necroptosis; however, there was no apparent variation in pyroptosis functionality. The scoring model achieved an AUC of 100% for sample differentiation. The RF PANoptosis diagnostic model demonstrated an AUC of 100% in the training set and 85.85% in the validation set for distinguishing AD. This study identified two PANoptosis-related hippocampal molecular subtypes of AD, identified key genes, and established machine learning models for subtype differentiation and discrimination of AD. We found that in the context of AD, PANoptosis may influence disease progression through the modulation of apoptosis and necrotic apoptosis.

Characterization of stem cell landscape and assessing the stemness degree to aid clinical therapeutics in hematologic malignancies

Hematological malignancies are a group of cancers that affect the blood, bone marrow, and lymphatic system. Cancer stem cells (CSCs) are believed to be responsible for the initiation, progression, and relapse of hematological malignancies. However, identifying and targeting CSCs presents many challenges. We aimed to develop a stemness index (HSCsi) to identify and guide the therapy targeting CSCs in hematological malignancies. We developed a novel one-class logistic regression (OCLR) algorithm to identify transcriptomic feature sets related to stemness in hematologic malignancies. We used the HSCsi to measure the stemness degree of leukemia stem cells (LSCs) and correlate it with clinical outcomes.We analyze the correlation of HSCsi with genes and pathways involved in drug resistance and immune microenvironment of acute myeloid leukemia (AML). HSCsi revealed stemness-related biological mechanisms in hematologic malignancies and effectively identify LSCs. The index also predicted survival and relapse rates of various hematologic malignancies. We also identified potential drugs and interventions targeting cancer stem cells (CSCs) of hematologic malignancies by the index. Moreover, we found a correlation between stemness and bone marrow immune microenvironment in AML. Our study provides a novel method and tool to assess the stemness degree of hematologic malignancies and its implications for clinical outcomes and therapeutic strategies. Our HSC stemness index can facilitate the precise stratification of hematologic malignancies, suggest possible targeted and immunotherapy options, and guide the selection of patients.

PERBANDINGAN AKURASI ALGORITMA K-NEAREST NEIGHBOR DAN LOGISTIC REGRESSION UNTUK PREDIKSI CUSTOMER CHURN

In this highly developed era, many transaction activities have shifted from offline to online, which is commonly called E-commerce. This study provides an overview of customer churn prediction using two algorithms that are often used, namely K-Nearest Neighbor and Logistic Regression. In this study, a comparison was made of the two algorithms in determining significant accuracy in predicting customer churn or unsubscribing customers. This research was conducted with three tests to compare the performance of the two algorithms from the distribution of test data by 30 percent, 25 percent, and 20 percent. The algorithms have a good performance on the distribution of test data by 20 percent, and Logistic Regression algorithm has a better performance at 82.89% than K-Nearest Neighbor algorithm which is only 81.84%.

Leveraging machine learning to enhance appointment adherence at a novel post-discharge care transition clinic.

This study applies predictive analytics to identify patients at risk of missing appointments at a novel post-discharge clinic (PDC) in a large academic health system. Recognizing the critical role of appointment adherence in the success of new clinical ventures, this research aims to inform future targeted interventions to increase appointment adherence. We analyzed electronic health records (EHRs) capturing a wide array of demographic, socio-economic, and clinical variables from 2168 patients with scheduled appointments at the PDC from September 2022 to August 2023. Logistic regression, decision trees, and eXtreme Gradient Boosting (XGBoost) algorithms were employed to construct predictive models for appointment adherence. The XGBoost machine learning model outperformed logistic regression and decision trees with an area under the curve (AUC) of 72% vs 65% and 67%, respectively, in predicting missed appointments, despite limited availability of historical data. Key predictors included patient age, number of days between appointment scheduling and occurrence, insurance status, marital status, and mental health and cardiac disease conditions. Findings underscore the potential of machine learning predictive analytics to significantly enhance patient engagement and operational efficiency in emerging healthcare settings. Optimizing predictive models can help balance the early identification of patients at risk of non-adherence with the efficient allocation of resources. The study highlights the potential value of employing machine learning techniques to inform interventions aimed at improving appointment adherence in a post-discharge transition clinic environment.