Research on Diabetes Risk Prediction Using Multiple Machine Learning Models

Chronic fatigue severely compromises the quality of life in patients with granulomatosis with polyangiitis (GPA). Traditional diagnostic methods are often time-consuming, relying on clinical expertise and detailed questionnaires. This study aimed to develop a machine learning model capable of predicting chronic fatigue in GPA patients based on clinical data, with a particular focus on improving diagnostic capacity in regions with limited access to specialists. This cross-sectional study collected data on fatigue (measured by the Modified Fatigue Impact Scale, MFIS), functional ability (Health Assessment Questionnaire, HAQ), disease activity (Birmingham Vasculitis Activity Score, BVAS), comorbidities, medication use, physical activity (International Physical Activity Questionnaire - Short Form, IPAQ-SF), and demographic characteristics. Four machine learning algorithms-logistic regression, decision tree, random forest, and extreme gradient boosting-were assessed using a 70/30 train-test split. Model performance was evaluated using area under the curve (AUC), accuracy, F1 score, recall, and precision. Statistical comparisons were performed using Welch's t-test and the Wilcoxon-Mann-Whitney U test for continuous variables, while the chi-square test or Fisher's exact test was applied to categorical variables, with significance set at P < 0.05. All analyses were conducted using R version 4.4.1 for Windows. Forty-five patients were assessed: 62.2% were female, with a median BMI of 27.72kg/m² (23.2-30.1), a median age of 55.5 years, and a median disease duration of 12.0 years (6.0-17.0). Fatigue was reported by 20 patients (MFIS score ≥ 38), and seven patients (15.5%) had active disease according to the BVAS, which was similar between the fatigued and no fatigued groups (P > 0.05). The fatigued group had more acute-phase reactants and prednisone use (P < 0.05). The tree-based models achieved an AUC of approximately 0.80, outperforming the other models. Tree-based models demonstrated superior predictive performance in identifying chronic fatigue. The Random Forest model, in particular, highlighted higher disability in activities of daily living (HAQ), older age, and longer disease duration as key predictors. Although the models performed well, additional data and incorporation of clinically relevant variables may further enhance predictive accuracy. Patients with GPA who experienced fatigue showed higher glucocorticoid use and elevated acute-phase reactants, despite similar levels of disease activity, suggesting mechanisms beyond inflammation. Machine learning shows strong potential as a clinical tool for fatigue identification, especially in settings with limited access to specialist care. Universal Trial Number (UTN): U1111-1271-6003; Brazilian Clinical Trials Registry (ReBEC): RBR-9n4z2hh. Registration date: January 18, 2022, and Plataforma Brasil (CAAE # 41762820.1.0000.0068).

Background and aimsThe birth weight of a newborn is a crucial factor that affects their overall health and future well-being. Low birth weight (LBW) is a widespread global issue, which the World Health Organization defines as weighing less than 2,500 g. LBW can have severe negative consequences on an individual’s health, including neonatal mortality and various health concerns throughout their life. To address this problem, this study has been conducted using BDHS 2017–2018 data to uncover important aspects of LBW using a variety of machine learning (ML) approaches and to determine the best feature selection technique and best predictive ML model.MethodsTo pick out the key features, the Boruta algorithm and wrapper method were used. Logistic Regression (LR) used as traditional method and several machine learning classifiers were then used, including, DT (Decision Tree), SVM (Support Vector Machine), NB (Naïve Bayes), RF (Random Forest), XGBoost (eXtreme Gradient Boosting), and AdaBoost (Adaptive Boosting), to determine the best model for predicting LBW. The model’s performance was evaluated based on the specificity, sensitivity, accuracy, F1 score and AUC value.ResultsResult shows, Boruta algorithm identifies eleven significant features including respondent’s age, highest education level, educational attainment, wealth index, age at first birth, weight, height, BMI, age at first sexual intercourse, birth order number, and whether the child is a twin. Incorporating Boruta algorithm’s significant features, the performance of traditional LR and ML methods including DT, SVM, NB, RF, XGBoost, and AB were evaluated where LR, had a specificity, sensitivity, accuracy and F1 score of 0.85, 0.5, 85.15% and 0.915. While the ML methods DT, SVM, NB, RF, XGBoost, and AB model’s respective accuracy values were 85.35%, 85.15%, 84.54%, 81.18%, and 84.41%. Based on the specificity, sensitivity, accuracy, F1 score and AUC, RF (specificity = 0.99, sensitivity = 0.58, accuracy = 85.86%, F1 score = 0.9243, AUC = 0.549) outperformed the other methods. Both the classical (LR) and machine learning (ML) models’ performance has improved dramatically when important characteristics are extracted using the wrapper method. The LR method identified five significant features with a specificity, sensitivity, accuracy and F1 score of 0.87, 0.33, 87.12% and 0.9309. The region, whether the infant is a twin, and cesarean delivery were the three key features discovered by the DT and RF models, which were implemented using the wrapper technique. All three models had the identical F1 score of 0.9318. However, “child is twin” was recognized as a significant feature by the SVM, NB, and AB models, with an F1 score of 0.9315. Ultimately, with an F1 score of 0.9315, the XGBoost model recognized “child is twin” and “age at first sex” as relevant features. Random Forest again beat the other approaches in this instance.ConclusionsThe study reveals Wrapper method as the optimal feature selection technique. The ML method outperforms traditional methods, with Random Forest (RF) being the most effective predictive model for Low-Birth-Weight prediction. The study suggests that policymakers in Bangladesh can mitigate low birth weight newborns by considering identified risk factors.

Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making. This study aimed to develop and validate a machine learning (ML)-based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support. A multicenter retrospective cohort study was conducted, including 1910 patients with AMI from the Affiliated Hospital of Guangdong Medical University (2005-2024). Patients were divided into training (n=1575) and testing (n=335) cohorts based on admission dates. For external validation, 1746 patients with AMI were included in the publicly available MIMIC-IV (Medical Information Mart for Intensive Care IV) database. Propensity score matching was adjusted for demographics, and the Boruta algorithm identified key predictors. A total of 7 ML algorithms-logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and neural networks-were trained using 10-fold cross-validation. The models were evaluated for the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, recall, F1-score, and decision curve analysis. Shapley additive explanations analysis ranked variable importance. Kaplan-Meier survival analysis evaluated the impact of GIB on short-term survival. Multivariate logistic regression assessed the relationship between coronary heart disease (CHD) and in-hospital GIB after adjusting for clinical variables. The RF model outperformed other ML models, achieving an area under the receiver operating characteristic curve of 0.77 in the training cohort, 0.77 in the testing cohort, and 0.75 in the validation cohort. Key predictors included red blood cell count, hemoglobin, maximal myoglobin, hematocrit, CHD, and other variables, all of which were strongly associated with GIB risk. Decision curve analysis demonstrated the clinical use of the RF model for early risk stratification. Kaplan-Meier survival analysis showed no significant differences in 7- and 15-day survival rates between patients with AMI with and without GIB (P=.83 for 7-day survival and P=.87 for 15-day survival). Multivariate logistic regression showed that CHD was an independent risk factor for in-hospital GIB (odds ratio 2.79, 95% CI 2.09-3.74). Stratified analyses by sex, age, occupation, marital status, and other subgroups consistently showed that the association between CHD and GIB remained robust across all subgroups. The ML-based RF model provides a robust and clinically applicable tool for predicting in-hospital GIB in patients with AMI. By leveraging routinely available clinical and laboratory data, the model supports early risk stratification and personalized preventive strategies.

ObjectiveAnalyze the psychological and clinical factors of clinically significant tinnitus (THI score ≥38) in patients with hearing loss, construct predictive models based on four machine learning (ML) algorithms, and compare the predictive performance of different models.MethodsPatients with hearing loss who visited the Department of Otolaryngology at Qinghai University between August 2024 and May 2025 were enrolled in this study. Clinical data were retrieved from the hospital’s electronic medical record system. The study outcome was the occurrence of clinically significant tinnitus. Predictive variables were screened using univariate analysis, the least absolute shrinkage and selection operator (LASSO) regression, and the Boruta algorithm. Four ML algorithms—logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM)—were applied to construct and validate predictive models. The area under the receiver operating characteristic curve (AUC) of each model in the validation set was compared using the DeLong test. Additionally, model performance metrics in the validation set were compared to identify the optimal model. Finally, the Shapley additive explanations (SHAP) algorithm was employed to interpret the best-performing model.ResultsNine key variables—age, hypertension, sleep disorder, anxiety, hearing loss severity, depression, noise exposure history, hearing side, and ototoxic drug use—were retained after LASSO and Boruta feature selection. Among the four ML models, the RF algorithm achieved the best predictive performance, with an AUC of 0.973 in the training set and 0.977 in the validation set, followed by XGBoost (AUC = 0.962 and 0.961, respectively). DeLong tests confirmed that RF significantly outperformed LR and SVM models (p < 0.001), while its difference from XGBoost was not significant. In the validation set, the RF model yielded the highest accuracy (0.923), sensitivity (0.929), specificity (0.914), precision (0.945), and F1-score (0.937). SHAP analysis indicated that hearing loss severity, age, and sleep disorder were the most influential predictors, suggesting that both auditory and non-auditory factors contribute substantially to the risk of clinically significant tinnitus.ConclusionThe RF model showed the best performance in predicting clinically significant tinnitus, with hearing loss severity, age, and sleep disorder identified as major predictors. Integrating auditory and psychological factors can improve early risk identification in patients with hearing loss.

Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental condition that affects millions of individuals globally. This study leverages the publicly available UCI Machine Learning ASD Screening Dataset, which contains behavioral questionnaire responses from children aged 4-11 years, to develop and compare multiple machine learning (ML) models for early ASD diagnosis. This study applies SHAP (SHapley Additive exPlanations) to interpret and analyze the feature importance of various attributes in the dataset. Advanced ML models like Extreme Gradient Boosting (XGBoost), Random Forest (RF), Extra Trees Classifier (ETC), Support Vector Machines (SVM), and Multilayer Perceptrons (MLPs) were implemented to evaluate and compare their performance in predicting early ASD. The developed ASD model underwent a rigorous evaluation through comprehensive performance metrics, including accuracy, precision, recall, F1-score, specificity, and Matthews correlation coefficient. Ranking-based metrics, such as area under the receiver operating characteristic curve (AUROC), provided additional insights into classification robustness, while 10-fold cross-validation ensured reliability and generalizability. Results indicate that XGBoost achieved the highest diagnostic performance (accuracy = 97.8%, AUROC = 0.99), followed closely by MLP, with RF and ETC offering balanced performance and interpretability. SHAP analysis identified key behavioral features influencing model predictions, enabling explainable AI-driven insights. These findings suggest that ML-based tools can support clinicians in identifying ASD earlier, allowing for timelier interventions in disability services, particularly in underserved populations. While results are promising, the use of secondary data and the lack of clinical validation remain limitations, highlighting the need for real-world pilot testing. These findings underscore the transformative potential of ML techniques in enhancing ASD diagnostics. Despite their promise, the results are constrained by reliance on secondary data and the absence of clinical validation, underscoring the need for real-world pilot testing. Nonetheless, incorporating these models into clinical practice could accelerate early detection, facilitate personalized interventions, and optimize resource allocation for improved ASD management outcomes.

Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Obesity is a growing public health concern, particularly among university students who are exposed to lifestyle changes, disordered eating habits, and reduced physical activity. The aim of this study is to classify obesity risk levels among university students using machine learning classification methods and to identify the most influential factors associated with this risk. The study sample consisted of data collected from 445 students studying at Çankırı Karatekin University. In this context, eight machine learning algorithms—Logistic Regression, Random Forest, Extra Trees, Support Vector Machines, K-Nearest Neighbor, Quadratic Discriminant Analysis, Naive Bayes, and Multilayer Perceptron—were compared to classify obesity risk. Class imbalance in the dataset was addressed using the Adaptive Synthetic Sampling (ADASYN) method applied exclusively to the training set. The models were evaluated using standard performance metrics, and the highest accuracy rate (96.26%) was achieved by the Random Forest model, followed by Logistic Regression with 94.77% accuracy. Variable importance analysis indicated that age, internet use scale score, and fast-food consumption frequency were the most influential factors in classification, while the low correlation between variables (|r| &amp;lt; 0.2) suggested that model performance was driven by the combined contribution of multiple features. Overall, the findings demonstrate that the balanced machine learning approach, particularly ensemble-based methods, can classify obesity risk with high accuracy and provide valuable insights for targeted prevention strategies among university students.

BackgroundLumbar drainage is widely used in the clinic; however, forecasting lumbar drainage-related meningitis (LDRM) is limited. We aimed to establish prediction models using supervised machine learning (ML) algorithms.MethodsWe utilized a cohort of 273 eligible lumbar drainage cases. Data were preprocessed and split into training and testing sets. Optimal hyper-parameters were archived by 10-fold cross-validation and grid search. The support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were adopted for model training. The area under the operating characteristic curve (AUROC) and precision-recall curve (AUPRC), true positive ratio (TPR), true negative ratio (TNR), specificity, sensitivity, accuracy, and kappa coefficient were used for model evaluation. All trained models were internally validated. The importance of features was also analyzed.ResultsIn the training set, all the models had AUROC exceeding 0.8. SVM and the RF models had an AUPRC of more than 0.6, but the ANN model had an unexpectedly low AUPRC (0.380). The RF and ANN models revealed similar TPR, whereas the ANN model had a higher TNR and demonstrated better specificity, sensitivity, accuracy, and kappa efficiency. In the testing set, most performance indicators of established models decreased. However, the RF and AVM models maintained adequate AUROC (0.828 vs. 0.719) and AUPRC (0.413 vs. 0.520), and the RF model also had better TPR, specificity, sensitivity, accuracy, and kappa efficiency. Site leakage showed the most considerable mean decrease in accuracy.ConclusionsThe RF and SVM models could predict LDRM, in which the RF model owned the best performance, and site leakage was the most meaningful predictor.

Background and objectiveThreatened abortion, a common pregnancy complication that often leading to abortion, is hard to predict due to its non-specific symptoms and difficulty in differentiating from other early pregnancy bleeding causes. Current diagnostic methods like serial ultrasounds and clinical monitoring are time-consuming and lack timeliness. To fill the gap in using advanced analytics for early detection and risk stratification, this study develops a machine learning (ML) model based on routine blood data to better predict threatened abortion, providing a reference for early detection and intervention.MethodsIn this study, we collected medical records from January 2022 to March 2024. We analyzed data from 1764 patients with threatened abortion and 1489 healthy controls. Blood test data of all participants were gathered. The Z-score normalization technique was applied to standardize blood routine indicators. This reduced the influence of outliers and noise. During hyperparameter optimization, ‘class_weight="balanced"’ was set to handle sample imbalance. The screening data was partitioned into a training set of 2928 cases (including the validation set) and a test set of 325 cases at an 8:1:1 ratio. Python was used to facilitate data transformation. Eight different ML algorithms—Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GBM), Extreme Gradient Boosting (XGB), Deep Neural Network (DNN), Decision Tree (DT) and Naive Bayes (NB)—were used to construct a threatened abortion prediction model. The prediction performances of the ML models were evaluated by calculating the area under the curve (AUC) values. We used the SHapley Additive exPlanation (SHAP) method to explain the models.ResultsComparatively, the DNN model showed the highest predictive performance among the eight models, with the highest AUC value of 96.76% and top metrics for accuracy (91.88%), specificity (91.62%), sensitivity (92.11%), and F1 score (92.48%). SHAP analysis identified Red Cell Distribution Width - Standard Deviation (RDW-SD), Platelet Distribution Width (PDW), Mean Platelet Volume (MPV), Red Cell Distribution Width - Coefficient of Variation (RDW-CV), Absolute Basophil Count (BAS#), Platelet Count (PLT), Mean Corpuscular Hemoglobin Concentration (MCHC) and Lymphocyte Percentage (LYM) as the most influential features in predicting threatened abortion, with PDW, RDW-CV, BAS#, PLT, MCHC and LYM positively contributing to the prediction, whereas RDW-SD and MPV had negative contributions.ConclusionsOur research on constructing a prediction model for threatened abortion through routine blood tests has revealed the great potential of ML algorithms in detecting threatened abortion. This algorithm is expected to analyse routine blood data to identify at-risk pregnancies at an early stage, significantly improving the early detection of this common pregnancy complication. It will assist healthcare providers in intervening earlier and reducing the incidence of abortion. However, before the model can be translated into routine clinical applications, more extensive validation studies are still needed.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12884-025-08030-z.

OOBJECTIVE: To develop a machine learning model for predicting mental illness risk in Chinese older adults using longitudinal data, facilitating early identification and intervention for high-risk individuals. METHODS: Data from 8,942 participants in the 2020 China Health and Retirement Longitudinal Study (CHARLS) were used to develop and validate predictive models. Four machine learning algorithms—logistic regression, naive Bayes, support vector machine, and random forest—were applied. Model parameters were optimized using the training set, and performance was evaluated on the testing set. RESULTS: LASSO regression selected 16 key features from 41 initial variables. The random forest (RF) model achieved the best performance on the test set, with specificity of 0.720, sensitivity of 0.543, accuracy of 0.632, and AUC of 0.680. CONCLUSION: Machine learning models, particularly RF, can effectively assess mental illness risk and offer interpretable insights into key predictive features.

Breast cancer is a highly prevalent malignant tumor among women worldwide, and early accurate diagnosis is crucial for improving prognosis. Traditional diagnostic methods have limitations such as high rates of misdiagnosis and missed diagnosis, while existing machine learning studies lack targeted strategies for screening nuclear morphological features. This study is the first to adopt Pearson correlation coefficient for targeted screening of breast cancer nuclear morphological features, retaining 25 features with an absolute correlation coefficient greater than 0.2 with diagnostic results; these features achieve a cumulative explained variance of 92.5% and accurately match the core pathological indicator of "nuclear pleomorphism". To address the class imbalance in the original dataset (569 cases, including 357 benign and 212 malignant cases), 143 synthetic samples were generated using the SMOTE algorithm, constructing a balanced dataset of 712 cases. After dividing the training and testing sets via 7:3 stratified sampling, a diagnostic model was built using a radial basis function (RBF) kernel support vector machine (SVM) and compared with a random forest model. Results showed that the SVM achieved an accuracy of 97.2%, precision of 98.1%, and recall of 96.3% on the testing set, with a 10-fold cross-validation average accuracy of 0.968 (with a fluctuation of 0.026); in contrast, the random forest model yielded corresponding indicators of 95.3%, 96.2%, 94.4%, and a 10-fold cross-validation average accuracy of 0.934 (with a fluctuation of 0.022), demonstrating that SVM significantly outperformed random forest. Additionally, the SVM achieved an AUC value of 0.968 on the ROC curve, showing excellent performance in the low false-positive range. Studies have shown that the SVM model based on features selected via the Pearson correlation coefficient exhibits significant advantages in breast cancer diagnosis. Compared with traditional diagnostic methods, such as mammography which has a misdiagnosis rate of 10%-15%, this model can reduce the risk of misdiagnosis by approximately 70%. Meanwhile, the model can effectively capture the clinical significance of microscopic nuclear features, not only providing a reliable tool for accurate breast cancer diagnosis but also promoting the in-depth integration of machine learning and pathological diagnosis.

Assessing treatment switch among patients with multiple sclerosis: A machine learning approach

Artificial lift systems, specifically Electrical Submersible Pumps and Sucker Rod Pumps, often face operational challenges due to high Gas-Oil Ratio, leading to premature tool failure and increased non-productive time. These issues underscore the necessity for efficient gas separation at upstream of the artificial lift pump to ensure uninterrupted operations. Downhole gas separators, situated upstream of the artificial lift pump, are designed to segregate gas from the fluid, reducing downtime. This study aims to predict the gas separation efficiency of the downhole gas separator using experimental data from a centrifugal separator and testing the generated models on experimental data for a gravity separator. To predict the separation efficiency of the downhole separator, multiple machine learning models were evaluated, and a comparative analysis was performed. The experimental test data obtained for the centrifugal packer-type separator was used for model training and experimental test data for the gravity separator was used for model testing (blind test). To conduct experiments, multiphase flow setup (31ft. (9.4m) long horizontal and 27ft. (8.2m) tall vertical section of multiphase flow setup) was used. In this study, seven regression methods, including multilinear (MLR), random forest (RF), support vector machine (SVM), ridge, lasso, k-nearest neighbor (KNN) and XGBoost, were examined to assess their ability to effectively predict the gas separation efficiency. Several performance metrics, including Root mean square error (RMSE), Mean absolute percentage error (MAPE) and R-squared were evaluated to select the efficient model. In-depth exploratory data analysis and feature engineering were performed to identify significant input parameters for model training. The scatterplot and correlation plot indicated the response variable were mostly affected by inlet liquid and gas volume flow. Therefore, liquid and gas volume flow at inlet were selected as two independent parameters to predict gas volume flow per minute at the outlet (GVFO) as the response variable. After data scaling, a comparative analysis of the seven incorporated regression models revealed that, the Random Forest model to be the most efficient for predicting GVFO, closely followed by the KNN model. The Random Forest model exhibits R-squared values of 96% and RMSE value of 112. Hyperparameter tuning and tenfold cross-validation ensured optimal values for tuning parameters and prevented oveffitting, respectively. This developed machine learning workflow enables the prediction of the gas separation efficiency of different type of downhole gas separator by using experimental test data from a centrifugal downhole separator. It provides critical findings through a comparative analysis of seven regression models. This research offers valuable insights that can aid in optimizing the performance of artificial lift systems.

Background: Breast cancer is the most common malignancy among women worldwide, underscoring the importance of early detection and accurate prognostication. Machine learning (ML) has emerged as a promising approach, offering powerful tools for analyzing complex datasets in breast cancer prediction and diagnosis. Objective: This study evaluates the predictive performance of diverse ML algorithms for breast cancer classification using publicly available datasets, focusing on accuracy, interpretability, and generalizability. Methods: The dataset included clinical and demographic variables such as age, menopausal status, tumor size, and lymph node involvement. Data preprocessing addressed missing values and class imbalance, with the Synthetic Minority Oversampling Technique (SMOTE) applied to improve sensitivity for the minority class. Feature engineering involved interaction terms and scaling of numerical variables. Multiple ML models—Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), and Neural Networks—were trained and evaluated. Performance was measured using sensitivity, F1-score, and AUC-ROC. Model interpretability was enhanced with SHapley Additive exPlanations (SHAP). Results: Random Forest achieved the best performance with an AUC-ROC of 0.9751, followed by Gradient Boosting (0.9242) and Neural Networks (0.9254). Logistic Regression and SVM yielded comparable results (0.9005 and 0.9344). Ensemble models showed higher accuracy and generalizability, particularly on external validation. Tumor size and lymph node involvement emerged as key predictors. SMOTE improved sensitivity across models. Conclusion: This study demonstrates the potential of ML in breast cancer prediction, emphasizing the effectiveness of ensemble methods and interpretability tools. Future work should focus on integrating ML into clinical practice for earlier detection and personalized treatment.

Prediction of chronic kidney disease (CKD) has emerged as a useful technique for early detection of at-risk persons and the introduction of appropriate management strategies. Machine learning and data-driven methods have been used in predictive modeling to examine massive databases of patient demographics, medical histories, test findings, and genetic information. These cutting-edge methods allow for the profiling of high-risk patients and the tailoring of healthcare administration approaches. Patient outcomes, complication rates, and healthcare system efficiency may all benefit greatly from CKD screening and prediction. Responsible use of CKD prediction algorithms, however, requires resolving issues with data availability, integration, and ethics. The area of medicine has benefited greatly from the use of Machine Learning (ML) methods, which have played an increasingly central role in illness prediction. In this study, we use a strategy that makes use of ML methods to construct effective tools for predicting the development of CKD. Multiple ML models are trained, and their results are compared using a variety of criteria. We applied five ML methods such as logistic regression (LR), Decision tree (DT), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). The LR and KNN have the highest accuracy with 99%.