CHRONIC KIDNEY DISEASE DETECTION USING ENSEMBLE LEARNING TECHNIQUES AND COMPARATIVE STUDY

The slow progression of chronic kidney disease (CKD) makes early detection and effective treatment the only ways to prevent the mortality rates. In this study, an amalgamation of ensemble machine learning (ML) models has been leveraged in an effort to support clinicians in their goal of faster, more accurate CKD recognition and detection. By detecting and assessing the risk variables early on, patients could limit the ramifications of this disease on their health. Consequently, binary categorization is the basis of this proposed ML technique. The CKD dataset, obtained from the UCI machine learning repository was utilized in this research consisting of 400 instances and 24 attributes, which is comprised of indicators, symptoms, and risk factors. 80% of the data was used to train the model, while the remaining 20% was used for testing. While utilizing the entire set of 25 features, the CatBoost and Random Forest models outperformed and outmatched the remaining algorithms with an accuracy of 99%. The Decision Tree, Ada Boost, and SVM algorithms were then used, with a constructive accuracy rate of 98%, 98%, and 95%, respectively. Furthermore, ROC curve for the five chosen ML models was used as a significant evaluation metric to help improve and supplement our understanding of the performance of the CKD categorization challenges. The results showed that the CatBoost model is more efficient and competent in successfully and accurately classifying a patient's CKD status, with an accuracy of 99.9% when critical attributes were used.

In recent years, online shopping has surged in popularity, with customer reviews becoming a crucial aspect of the decision-making process. Reviews not only help potential customers make informed choices, but also provide businesses with valuable feedback and build trust. In this study, we conducted a thorough analysis of the Amazon reviews dataset, which includes several product categories. Our primary objective was to accurately classify sentiments using natural language processing, machine learning, ensemble learning, and deep learning techniques. Our research workflow encompassed several crucial steps. We explore data collection procedures; preprocessing steps, including normalization and tokenization; and feature extraction, utilizing the Bag-of-Words and TF–IDF methods. We conducted experiments employing a variety of machine learning algorithms, including Multinomial Naive Bayes, Random Forest, Decision Tree, and Logistic Regression. Additionally, we harnessed Bagging as an ensemble learning technique. Furthermore, we explored deep learning-based algorithms, such as CNNs, Bidirectional LSTM, and transformer-based models, like XLNet and BERT. Our comprehensive evaluations, utilizing metrics such as accuracy, precision, recall, and F1 score, revealed that the BERT algorithm outperformed others, achieving an impressive accuracy rate of 89%. This research provides valuable insights into the sentiment analysis of Amazon reviews, aiding both consumers and businesses in making informed decisions and enhancing product and service quality.

Postpartum depression (PPD) presents a significant mental health challenge for mothers following childbirth. While the precise cause of this condition remains unknown, preventive measures and treatments are available. This study aims to employ ensemble learning techniques, utilizing C4.5 decision tree (DT), gradient boosting tree (GBT), and extreme gradient boosting (XGBoost), to predict the occurrences of PPD in the Banjarmasin, South Kalimantan, Indonesia. The predictive model developed encompasses a dataset comprising 317 records gathered from postpartum mothers in hospitals, community health services, and midwifery clinics (referred to as Model 1). Furthermore, resampling techniques (Model 2) were employed to address class imbalance. Additionally, feature selection including forward selection and backward elimination (Model 3) were implemented to enhance model performance. The findings reveal that XGBoost, combined with resampling methods, achieved the highest accuracy rate at 87.57%. Feature selection identified five crucial factors associated with PPD incidence: marital status, number of living children, history of depression, fear of delivery, and family relationships. The utilization of ensemble learning strategies for PPD prediction yields reliable outcomes that can be applied within clinical settings. Exploring alternative ensemble learning strategies such as random forest and adaptive boosting could further optimize model performance and warrant consideration in future research endeavours.

Ensemble learning strategies, especially boosting and bagging decision trees, have demonstrated impressive capacities to improve the prediction accuracy of base learning algorithms. Further gains have been demonstrated by strategies that combine simple ensemble formation approaches. We investigate the hypothesis that the improvement in accuracy of multistrategy approaches to ensemble learning is due to an increase in the diversity of ensemble members that are formed. In addition, guided by this hypothesis, we develop three new multistrategy ensemble learning techniques. Experimental results in a wide variety of natural domains suggest that these multistrategy ensemble learning techniques are, on average, more accurate than their component ensemble learning techniques.

Groundwater is considered to be an essential resource for freshwater in Chhattisgarh. The importance of groundwater leads to a concern about water quality because the bad quality of water may lead to an unhealthy life. In this paper, we have used the numerous techniques of Ensemble Learning- (namely, Stacking, Bagging, and Boosting) for the classification of groundwater based on the Water Quality Index. The Stacking has been done by combining the following Classification Models Logistic Regression, k-Nearest Neighbors, Decision Tree, Support Vector Machine, and Naive Bayes. Bagging has been done using Bagged Decision Trees, Random Forest, and Extra Trees. Boosting has been performed using AdaBoost and Stochastic Gradient Boosting. Further, a comparative analysis of the performance of these Ensemble Learning Techniques has been presented. It has been observed that the maximum accuracy for classification has been given by the Stacking, Bagged Decision Trees, and Gradient Boosting with an accuracy of 0.9953.

Using speech data, it is difficult to learn through machine learning how to diagnose Parkinson's disease (PD) and evaluate the effects of treatment. For this issue, the article has developed a three-stage PD discovery method. The base classifiers used in the initial stage are logistic regression (LR), K-nearest neighbor (KNN), naive bayes (NB), support vector machine (SVC), and decision tree (DT). The second stage, or stack model, is a meta-model that combines all of the classifiers mentioned earlier. The third stage ensemble model consists of Bagging, AdaBoost, Random Forest (RF), and Gradient Boosting (GBC) components. The RF and GBC classifiers are utilized to estimate the most important features from the PD dataset. The models' validation has been evaluated using the confusion matrix and validation metrics like precision, recall, and F1 score. Out of all the ensemble models, the GBC—the third model—had the highest accuracy with testing data—97.43%. KNN from the base model and stacking from the meta-model, on the other hand, had the highest accuracy, with 94.87% each. Out of all the models mentioned in this manuscript, the GBC is the only ensemble model classifier with the highest accuracy. The proposed classifier appears to be an extremely useful model for the discovery of Parkinson's disease, as demonstrated by the exploratory findings and factual analyses.

Chronic kidney disease (CKD) involves numerous variables, but only a few significantly impact the classification task. The statistically equivalent signature (SES) method, inspired by constraint-based learning of Bayesian networks, is employed to identify essential features in CKD. Unlike conventional feature selection methods, which typically focus on a single set of features with the highest predictive potential, the SES method can identify multiple predictive feature subsets with similar performance. However, most feature selection (FS) classifiers perform suboptimally with strongly correlated data. The FS approach faces challenges in identifying crucial features and selecting the most effective classifier, particularly in high-dimensional data. This study proposes using the Least Absolute Shrinkage and Selection Operator (LASSO) in conjunction with the SES method for feature selection in CKD identification. Following this, an ensemble deep-learning model combining long short-term memory (LSTM) and gated recurrent unit (GRU) networks is proposed for CKD classification. The features selected by the hybrid feature selection method are fed into the ensemble deep-learning model. The model’s performance is evaluated using accuracy, precision, recall, and F1 score metrics. The experimental results are compared with individual classifiers, including decision tree (DT), Random Forest (RF), logistic regression (LR), and support vector machine (SVM). The findings indicate a 2% improvement in classification accuracy when using the proposed hybrid feature selection method combined with the LSTM and GRU ensemble deep-learning model. Further analysis reveals that certain features, such as HEMO, POT, bacteria, and coronary artery disease, contribute minimally to the classification task. Future research could explore additional feature selection methods, including dynamic feature selection that adapts to evolving datasets and incorporates clinical knowledge to enhance CKD classification accuracy further.

Aim: Chronic kidney disease (CKD) is among the main 20 reasons for death worldwide and influences around 10% of the world grown-up populace. CKD is an issue that upsets typical kidney work. The main objective of this study aims to find the best-suited algorithm that will give us the most ideal prediction. We will be comparing Novel Decision Tree with Linear Regression to find out which of these can give us the best accuracy. Material and Methods: The study used 322 samples with Novel Decision Tree and Linear Regression is executed with varying training and testing splits for predicting the accuracy for kidney disease prediction with the G-power value of 80% and the kidney datasets were collected from various web sources with recent study findings and threshold 0.05%, confidence interval 95% mean and standard deviation. The performance of the classifiers are evaluated based on their accuracy rate using the chronic kidney disease dataset. Results: The accuracy of predicting kidney disease in Novel Decision Tree (96.66%) and Linear Regression (85.25%) is obtained. There is a statistical 2-tailed significant difference in accuracy for two algorithms is 0.000 (p<0.05) by performing independent samples t-tests. Conclusion: This study concludes that the Prediction of Kidney disease using the Novel Decision Tree (DT) algorithm appears to be significantly better than the Linear Regression(LR) with improved accuracy.

Purpose: To evaluate the accuracy and clinical usability of an identification model using ensemble deep learning for 130 dental implant types. Materials and Methods: A total of 28,112 panoramic radiographs were obtained from 30 domestic and foreign dental clinics. From these panoramic radiographs, 45,909 implant fixture images were extracted and labeled based on electronic medical records. Dental implants were classified into 130 types according to the manufacturer, the manufacturer's implant system, and the diameter and length of the implant fixture. Regions of interest were manually cropped, and data augmentation was performed. According to the minimum number of images collected per implant type, the datasets were classified into three sets: an overall total of 130 and two subsets that consisted of 79 and 58 types. EfficientNet and Res2Next algorithms were used for image classification in deep learning. After testing the performance of the two models, the ensemble learning technique was applied to improve accuracy. The top-1 accuracy, top-5 accuracy, precision, recall, and F1 scores were calculated according to algorithms and datasets. Results: For the 130 types, the top-1 accuracy, top-5 accuracy, precision, recall, and F1 scores were 75.27, 95.02, 78.84, 75.27, and 74.89, respectively. In all cases, the ensemble model performed better than EfficientNet and Res2Next. When using the ensemble model, the accuracy increased as the number of types decreased. Conclusion: The ensemble deep learning model for the identification of 130 types of dental implants showed higher accuracy than the existing algorithms. To further improve the performance and clinical usability of the model, images with higher quality and fine-tuned algorithms optimized for implant identification are required.

Child mortality is a big problem around the world, especially in low- and middle-income nations where there are big differences in health care and social conditions. This investigation seeks to create a predictive model for child mortality and pinpoint the key factors that significantly contribute to it, employing machine learning (ML) methodologies. The dataset includes various features such as parental age, maternal education, birth weight, wealth index, and access to healthcare services. Thirteen machine learning classifiers were used, categorized into four model groups: Traditional Models (Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Naive Bayes), Tree-Based Models (Decision Tree, Random Forest, Extra Trees), Boosting Models (AdaBoost, Gradient Boosting, XGBoost), and Ensemble Learning Models (Soft Voting, Hard Voting, Stacking). The efficacy of each model was assessed using classification metrics, including Accuracy, Precision, Recall, and F1-Score within a 10-fold cross-validation framework to guarantee robustness. Results indicate that ensemble models, particularly AdaBoost, achieved the highest predictive accuracy, with perfect scores across all metrics (1.00). XGBoost and Stacking also demonstrated strong and consistent performance. The findings indicate that ensemble learning methods are effective in predicting child mortality and can assist policymakers and healthcare planners in identifying high-risk populations and implementing targeted interventions to reduce child mortality.

Accurate and early diagnosis of HIV/AIDS is critical for effective treatment and reducing disease transmission. This study evaluates the performance of several machine learning models, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes, for classifying HIV/AIDS infection status. A dataset comprising 50,000 samples was used, and models were assessed based on accuracy, precision, recall, and F1 score using stratified ten-fold cross-validation to ensure robust evaluation. The results reveal significant trade-offs between sensitivity and specificity across the models. Gradient Boosting achieved the highest accuracy (70.85%) and precision (57.81%), making it suitable for confirmatory testing where minimizing false positives is critical. Conversely, Naive Bayes demonstrated the highest recall (57.99%) and F1 score (51.04%), emphasizing its effectiveness in early-stage diagnostics where sensitivity is paramount. SVM exhibited the highest precision (59.87%) but struggled with recall (11.28%), reflecting its conservative nature in classifying positive cases. These findings underscore the importance of selecting models tailored to specific diagnostic objectives. While Naive Bayes is ideal for comprehensive screening programs, Gradient Boosting and SVM are better suited for confirmatory testing. This research provides valuable insights into the strengths and limitations of machine learning models for medical diagnostics, paving the way for developing more robust, hybrid approaches to optimize sensitivity and specificity in HIV/AIDS classification.

Gender classification is one of the major challenges in the domain of speech analysis now-a-days. Identification of gender from acoustic properties of voice such as mean, median, and frequency, is of high importance. Machine learning is used to solve this problem because it gives promising results for classification techniques. There are several algorithms that can be used to predict the gender using acoustic properties. In this work, classifiers are evaluated using 6 different machine learning algorithms. These algorithms include K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), Support Vector Machine Using Poly kernel and Gradient Boosting (GB). For the same voice sample dataset, the accuracy, recall, precision and F1 Score are computed for all the above-mentioned classifiers. The accuracy obtained from KNN is 96.71, DT is 93.33%, Random Forest is 97.72%, SVM is 98.48%, SVM using Poly Kernel is 97.72%, Gradient Boosting is 96.96%. The predicted values of the considered evaluation metrices on test data for all above classifiers evidenced the supremacy of SVM classifier as it is best fitted model for gender identification of acoustic. SVM attained the accuracy of 98.48% which is the highest among all.

Chronic kidney disease (CKD) remains a major global health concern, often progressing silently until advanced stages. Early detection is therefore critical to improving outcomes. This study focuses on identifying the most important predisposing risk factors for CKD and optimizing prediction performance using a stacking machine learning ensemble. Two datasets were analyzed: the UCI CKD dataset and a synthetically simulated CKD dataset designed to mirror real-world variability. A leakage-safe preprocessing pipeline was implemented, including median and mode imputation for missing values, Z-score capping for outliers, min–max normalization, and class balancing through the Synthetic Minority Over-sampling Technique (SMOTE). Feature selection was performed using six complementary approaches: Logistic Regression (LR), Recursive Feature Elimination (RFE), Random Forest (RF), Mutual Information (MI), Chi-Square (χ²), and Principal Component Analysis (PCA) with a majority-vote strategy used to identify features consistently recognized as predictive. For the UCI CKD dataset, the common risk factors were serum creatinine, hemoglobin, packed cell volume, red blood cell count, specific gravity, albumin, sugar, hypertension, diabetes mellitus, and appetite. For the simulated CKD dataset, key predictors included blood glucose random, blood urea, serum creatinine, potassium, hemoglobin, packed cell volume, specific gravity, albumin, red blood cells, pus cell clumps, appetite, pedal edema, and anemia. Using these selected features, the stacking ensemble achieved 100.0% accuracy on the UCI CKD dataset and 96.7% accuracy on the simulated dataset, both with negligible misclassification rates. Bootstrap confidence intervals confirmed the robustness of these results. The findings highlight that combining systematic feature selection with stacking significantly improves predictive accuracy while maintaining interpretability. This integrated framework offers a reliable tool for early CKD detection and can support clinical decision-making in real-world healthcare settings. Future work will focus on expanding validation across multi-site data and developing a clinical decision support interface. IJSS, Vol. 25(2), November, 2025, pp 1-32

The kidneys are one of the most important organs including the excretion system in humans. The kidneys are responsible for maintaining blood concentrations to remain constant (homeostatic) and help to control blood pressure (BP). If the task of the kidney is not functioning properly it will cause kidney failure. In the past decade, data mining methods have been used to diagnose kidney failure. The dataset used to predict kidney failure was successfully summarized by Soundarapandian, and was named the Chronic Kidney Disease (CKD) dataset. But the data in the CKD dataset contains missing value and high dimension data (original data) so that it affects the evaluation results on classification. This research proposes methods in preprocessing data, namely modus in every class (MEC) method to solve missing value problems, and the weight information gain (WIG) method for solving high dimensional data problems, the proposed method is named the MEC + WIG method. The MEC + WIG method will be compared with the original method and the MEC method and evaluated based on the accuracy of the traditional classification method (k-NN, Naïve Bayes, C4.5, and CART). The results showed that the average accuracy of the MEC + WIG method was better than the original method and the MEC method, with the average accuracy of the MEC + WIG method at 98.13%, while the average value of the accuracy of the original method and MEC respectively amounting to 88.56% and 92.88%. There were significant differences between the three methods when tested using Friedman test with a p-value of 0.02. It can be concluded that the MEC + WIG method can improve the performance of traditional methods k-NN, Naive Bayes, C4.5 and CART by overcoming the problem of missing value and data high dimension.

Chronic kidney disease (CKD) is a serious global health problem with high mortality rates, often due to late diagnosis. Early detection and classification are essential to improve treatment outcomes and slow disease progression. This study evaluates the performance of four machine learning algorithms—linear discriminant analysis (LDA), Naïve Bayes, C4.5 decision tree, and Random Forest—in classifying CKD using a Kaggle dataset containing 1,659 instances and 52 features, covering demographic, lifestyle, and clinical data. After data pre-processing, the classification accuracies of the algorithms were assessed. LDA showed the highest accuracy at 92.8%, followed by Naïve Bayes (92.1%), C4.5 (92.0%), and Random Forest (91.9%) before hyperparameter tuning. After tuning, C4.5 achieved the highest accuracy of 92.5%, followed by Random Forest (92.2%), with Naïve Bayes remaining at 92.1%. However, even after tuning, LDA remained the most accurate, demonstrating superior performance. The key features contributing to CKD classification were serum creatinine, glomerular filtration rate (GFR), muscle cramps, protein in urine, fasting blood sugar, itching, systolic blood pressure, blood urea nitrogen (BUN), HbA1c, edema, total cholesterol, body mass index (BMI), and gender. These findings confirm that LDA outperforms other algorithms in CKD classification without the need for tuning, emphasizing the value of machine learning in improving early diagnosis and management of CKD.