Freeway traffic state classification using vehicle trajectory data

Purpose: This research aims to examine the performance of the random forest algorithm in diabetes risk classification with data balancing using the Synthetic Minority Oversampling Technique (SMOTE) method to improve the representation of minority classes and increase the prediction accuracy value. Methods: The study used the Behavioral Risk Factor Surveillance System (BRFSS) dataset, obtained from Kaggle, which contains health-related survey data used to identify individuals at risk of diabetes. The Random Forest algorithm was applied to classify diabetes. To balance the data, the SMOTE method was used. The model’s performance was evaluated using 10-fold cross-validation by comparing result before and after SMOTE. Result: The results showed that the application of the SMOTE method improved the performance of the Random Forest classification model, especially in minority classes. Model performance in minority classes without SMOTE had poor evaluation metrics with precision of 49%, recall of 18%, and F1-score of 26%. After applying SMOTE, these values increased to precision of 96%, recall of 88%, and F1-score of 92%. Representing improvements of 47 percentage points in precision, 70 points in recall, and 66 points F1-score. The overall accuracy of the Random Forest model also increased from 86% to 92%, showing a 6 percentage point improvement. Novelty: This study use integrating the Random Forest algorithm with the SMOTE technique and validating the results using 10-fold cross-validation. The combination significantly improves minority class prediction performance in early diabetes detection, addressing the common limitations of previous studies in handling imbalanced datasets effectively.

A traffic state recognition model based on feature map and deep learning

Musculoskeletal disorders (MSDs) are a major health concern for children. Traditional assessment methods, which are based on subjective assessments, may be inaccurate. The main objective of this research is to evaluate Synthetic Minority Over-sampling Technique(SMOTE)-based machine learning algorithms for predicting MSDs in elementary school students with an unbalanced dataset. This study is the first to use these algorithms to increase the accuracy of MSD prediction in this age group. This cross-sectional study was conducted in 2024 on 438 primary school students (boys and girls, grades 1 to 6) in Hamedan, Iran. Random sampling was performed from 12 public and private schools. The dependent variable was the presence or absence of MSD, assessed using the Cornell questionnaire. Given the imbalanced nature of the data, SMOTE-based techniques were applied. Finally, the performance of six machine learning algorithms, including Random Forest (RF), Naive Bayes (NB), Artificial Neural Network (ANN), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) was compared based on five SMOTE family techniques, including SVM-SMOTE, SMOTE-NC, ADASYN, Borderline SMOTE, and the SMOTE method, for predicting musculoskeletal disorders in primary school students. This study analyzed 438 students, with 28 (6.39%) having musculoskeletal disorders (MSD). Machine learning models were tested on both imbalanced and SMOTE-balanced data. Before SMOTE, sensitivity was low (e.g., DT: 18%), but after applying SMOTE, it improved significantly (DT: 85%). Area Under the Curve (AUC) for RF and XGBoost increased from 65 to 99%. Among SMOTE-based techniques, SMOTE-NC achieved the highest accuracy for XGBoost (93.65%), RF (93.41%), ANN (92.80%), SVM (90.24%), and DT (86.58%). Borderline-SMOTE yielded the highest accuracy (96%) for XGBoost and RF. RF and XGBoost performed best overall. Key predictors of MSD were regional facilities, BMI, and gender. The findings of this study demonstrate the effectiveness of SMOTE in addressing class imbalance and improving the accuracy of MSD prediction among students. Overall, RF and XGBoost outperformed other algorithms. Additionally, key factors influencing MSD included regional facilities, body mass index (BMI), and gender, highlighting the significance of environmental and physiological factors in the development of this disorder.

Conventional wine quality assessment remains reliant on subjective expert judgment, which introduces potential bias and inconsistency in quality control processes. This study aims to develop an objective and automated machine learning-based classification model to enhance the accuracy of wine quality prediction. To address the issue of class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, along with ANOVA F-test-based feature selection to optimize model performance. The White Wine Quality dataset from the UCI Machine Learning Repository (4,898 samples, 11 numerical features) was utilized to evaluate five classification algorithms: Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Before SMOTE application, the Random Forest model achieved an accuracy of only 67.55%. After implementing SMOTE and parameter tuning, the Random Forest (Tuned) model demonstrated the best performance with 90.29% accuracy, 89.99% precision, 90.29% recall, and 89,97%. % F1-score. Additionally, Decision Tree and KNN algorithms also exhibited notable improvements. SMOTE effectively balanced extreme minority class representations (quality levels 3 and 9). The most influential features in quality classification were alcohol content, density, and chlorides. These findings indicate that the proposed framework offers a reliable, objective, and scalable solution for automated wine quality control in industrial production environments.

Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs. The CCPM first removes outliers by using outlier detection methods such as density-based spatial clustering of applications with noise (DBSCAN) and isolation forest (iForest) and by increasing the number of cases in the dataset in a balanced way, for example, through synthetic minority over-sampling technique (SMOTE) and SMOTE with Tomek link (SMOTETomek). Finally, it employs random forest (RF) as a classifier. Thus, CCPM lies on four scenarios: (1) DBSCAN + SMOTETomek + RF, (2) DBSCAN + SMOTE+ RF, (3) iForest + SMOTETomek + RF, and (4) iForest + SMOTE + RF. A dataset of 858 potential patients was used to validate the performance of the proposed method. We found that combinations of iForest with SMOTE and iForest with SMOTETomek provided better performances than those of DBSCAN with SMOTE and DBSCAN with SMOTETomek. We also observed that RF performed the best among several popular machine learning classifiers. Furthermore, the proposed CCPM showed better accuracy than previously proposed methods for forecasting cervical cancer. In addition, a mobile application that can collect cervical cancer risk factors data and provides results from CCPM is developed for instant and proper action at the initial stage of cervical cancer.

Diabetes, or high blood sugar, is a chronic condition that needs careful monitoring. If left untreated, it can lead to severe complications. This research aims to accurately diagnose diabetes, addressing the issue of class imbalance in the dataset, which can affect the model's classification accuracy. The goal is to improve diabetes classification accuracy using balancing methods, specifically the Synthetic Minority Over-sampling Technique (SMOTE) and Random Oversampling. These methods are applied to data from patients diagnosed with diabetes and those who do not have the disease.The initial step in the research involved addressing class imbalance by applying SMOTE and random oversampling to generate synthetic samples for the minority class. This was followed by data normalization using the min-max normalization method. Subsequently, the Random Forest Classifier was used to train the model for classification. The results demonstrate that this approach enhances the model's ability to identify diabetes cases, achieving an accuracy of 96%. This represents a 1% improvement over the accuracy of 95% reported in previous research.

Background: Credit is essential in banking operations, facilitating investment, corporate expansion, and financial satisfaction. Credit risk may emerge if the borrower defaults on payment commitments. Objective: This study aims to evaluate an individual's creditworthiness by classifying and assessing their eligibility for credit. Methods: This study uses the Random Forest technique to categorize credit risk evaluation. Random Forest is a decision tree technique recognized for its high accuracy in data classification, utilizing an ensemble method of many decision trees. Before executing the classification process, issues frequently arise when data cannot be directly processed due to class imbalance. This study employs the SMOTE (Synthetic Minority Over-sampling Technique) algorithm to address class imbalance. The SMOTE algorithm is a method that emphasizes oversampling and is designed to augment the data in the minority class by generating synthetic data that aligns with the minority class data. The findings indicated that the ideal ratio for partitioning training and testing data was 80:20, and implementing the SMOTE technique within Random Forest enhanced performance assessment. Results: This research contributes to improving the accuracy of credit risk classification using the Random Forest algorithm, which effectively handles complex data and is supported by the implementation of SMOTE to overcome the class imbalance in the data. The classification accuracy value rose from 91.54% to 94.41%. The precision value rose from 90.83% to 97.03%, while the recall value increased from 60.26% to 91.55%. Conclusion: This method helps banks identify high-risk debtors more objectively and efficiently and supports appropriate credit decision-making.

Predicting credit default risk is important to financial institutions, as accurately predicting the likelihood of a borrower defaulting on their loans will help to reduce financial losses, thereby maintaining profitability and stability. Although machine learning models have been used in assessing large applications with complex attributes for these predictions, there is still a need to identify the most effective techniques for the model development process, including the technique to address the issue of data imbalance. In this research, we conducted a comparative analysis of random forest, decision tree, SVMs (Support Vector Machines), XGBoost (Extreme Gradient Boosting), ADABoost (Adaptive Boosting) and the multi-layered perceptron, to predict credit defaults using loan data from LendingClub. Additionally, XGBoost was used as a framework for testing and evaluating various techniques. Moreover, we applied this XGBoost framework to handle the issue of class imbalance observed, by testing various resampling methods such as Random Over-Sampling (ROS), the Synthetic Minority Over-Sampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), Random Under-Sampling (RUS), and hybrid approaches like the SMOTE with Tomek Links and the SMOTE with Edited Nearest Neighbours (SMOTE + ENNs). The results showed that balanced datasets significantly outperformed the imbalanced dataset, with the SMOTE + ENNs delivering the best overall performance, achieving an accuracy of 90.49%, a precision of 94.61% and a recall of 92.02%. Furthermore, ensemble methods such as voting and stacking were employed to enhance performance further. Our proposed model achieved an accuracy of 93.7%, a precision of 95.6% and a recall of 95.5%, which shows the potential of ensemble methods in improving credit default predictions and can provide lending platforms with the tool to reduce default rates and financial losses. In conclusion, the findings from this study have broader implications for financial institutions, offering a robust approach to risk assessment beyond the LendingClub dataset.

Diagnosing a parasitic disease is a very difficult job in clinical practice. In this study, we constructed a machine learning model for diagnosis prediction using patient information. First, we diagnosed whether a patient has a parasitic disease. Next, we predicted the proper diagnosis method among the six types of diagnostic terms (biopsy, endoscopy, microscopy, molecular, radiology, and serology) if the patient has a parasitic disease. To make the datasets, we extracted patient information from PubMed abstracts from 1956 to 2019. We then used two datasets: the prediction for parasite-infected patient dataset (N = 8748) and the prediction for diagnosis method dataset (N = 3780). We then compared four machine learning models: support vector machine, random forest, multi-layered perceptron, and gradient boosting. To solve the data imbalance problem, the synthetic minority over-sampling technique and TomekLinks were used. In the parasite-infected patient dataset, the random forest, random forest with synthetic minority over-sampling technique, gradient boosting, gradient boosting with synthetic minority over-sampling technique, and gradient boosting with TomekLinks demonstrated the best performances (AUC: 79%). In predicting the diagnosis method dataset, gradient boosting with synthetic minority over-sampling technique was the best model (AUC: 87%). For the class prediction, gradient boosting demonstrated the best performances in biopsy (AUC: 88%). In endoscopy (AUC: 94%), molecular (AUC: 90%), and radiology (AUC: 88%), gradient boosting with synthetic minority over-sampling technique demonstrated the best performance. Random forest demonstrated the best performances in microscopy (AUC: 82%) and serology (AUC: 85%). We calculated feature importance using gradient boosting; age was the highest feature importance. In conclusion, this study demonstrated that gradient boosting with synthetic minority over-sampling technique can predict a parasitic disease and serve as a promising diagnosis tool for binary classification and multi-classification schemes.

Credit Card Fraud Detection is one of the vital issues nowadays which needs to be tackled urgently. In today's world, everyone is shifting to an online and cashless world for easiness in the transaction. However, a colossal fraud scheme is running on the other side of this easiness. Daily, many people fall into this trap. This research work is a little contribution to solving this issue. This academic study uses data from the real world to find fraudulent transactions using Machine Learning techniques such as Decision Trees, Logistics Regression, and Random Forest. Furthermore, Synthetic Minority Oversampling Technique is employed to solve the dataset's imbalance issue. Following that, the effectiveness of machine learning methods is compared by using the “With SMOTE” and “Without SMOTE” techniques.

One of the problems that are often faced by classifier algorithms is related to the problem of imbalanced data. One of the recommended improvement methods at the data level is to balance the number of data in different classes by enlarging the sample to the minority class (oversampling), one of which is called The Synthetic Minority Oversampling Technique (SMOTE). SMOTE is commonly used to balance data consisting of two classes. In this research, SMOTE was used to balance multi-class data. The purpose of this research is to balance multi-class data by applying SMOTE repeatedly. This iterative process needs to be applied if the number of unbalanced data classes is more than two classes, because the one-time SMOTE process is only suitable for binary classification or the number of unbalanced data classes is only one class. To see the performance of iterative SMOTE, the SMOTE datasets were classified using a neural network, k-NN, Nave Bayes, and Random Forest and the performance measures were measured in terms of accuracy, sensitivity, and specificity. The experiment in this research used the Glass Identification dataset which had six classes, and the SMOTE process was repeated five times. The best performance was achieved by the Random Forest classifier method with accuracy = 86.27%, sensitivity = 86.18%, and specificity = 95.82%. The result of experiment present that repeated SMOTE results can increase the performance of classification.

The research paper undertakes a comprehensive comparative assessment of various Synthetic Minority Over-sampling Technique (SMOTE) variants in the context of medical datasets. The study is dedicated to mitigating the challenges posed by imbalanced data in medical applications. The performance of SMOTE, SMOTE-ENN, Borderline SMOTE, SMOTE-Tomek Links, Safe Level SMOTE, Density-based SMOTE, SMOTE-Rough Set Theory, and a developed BES-Optimized SMOTE are evaluated across five distinct medical datasets such as heart disease, arrhythmia, sick euthyroid, hypothyroid and breast cancer. Additionally, the K-Nearest Neighbors (KNN), Decision Forest (DF), Random forest (RF), and Support Vector Machine (SVM) algorithms are utilized to analyze the performance of SMOTE variants on each dataset, where the developed Bald Eagle search (BES) optimized SMOTE shows superior performance than other SMOTE variants, indicating its effectiveness in generating balanced scaled datasets. Subsequently, the BES-optimized SMOTE is chosen as the most effective SMOTE variant to scale and balance the respective datasets. Following a thorough examination of results, datasets that are appropriately scaled with the selected SMOTE variant are employed for ensuing research endeavors. These refined and balanced datasets will serve as the foundation for performance analysis of our future work on efficient privacy preserving KNN classification. To facilitate this, the scaled datasets in this work will be encrypted using the Paillier Encryption Scheme.

Fetal health during pregnancy is a crucial aspect in ensuring the optimal growth and development of a child, particularly during the golden period of life from the womb to the age of two. In the medical field, monitoring fetal conditions is vital to detect potential risks as early as possible. One of the tools commonly used in this process is cardiotocography (CTG), which provides essential data on fetal heart activity and movement. With technological advancements, machine learning-based approaches are increasingly being utilized to process CTG data more effectively. However, a major challenge in classifying medical data such as CTG lies in class imbalance, where the distribution between majority and minority classes is uneven. This study evaluates the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in addressing this imbalance and assesses the performance of the Random Forest algorithm in classifying fetal health conditions. The results show that the combination of SMOTE and Random Forest achieves the best performance compared to other methods, with an accuracy of 94.40%, precision of 94.45%, recall of 94.40%, and an F1-score of 94.38%. These findings indicate that SMOTE is effective in improving the representation of minority classes, while Random Forest demonstrates superior and consistent classification performance on CTG data

Gender-based violence can include sexual, physical, mental, and economic harm inflicted in public or in private. This violence also has a direct psychological effect, physical and financial consequences, and it has multiple underlying reasons, such as social, economic, cultural, political, and religious aspects. By applying multiple resampling techniques, this study aims to improve the precision and accuracy of supervised machine learning classifications of gender-based violence (GBV) using the SDHS dataset. The class imbalance between GBV-positive and GBV-negative instances makes it very challenging to produce reliable classification machine learning models. To address this issue, oversampling machine learning approaches, including synthetic minority over-sampling technique (SMOTE), adaptive synthetic (ADASYN), and random over-sampling (ROS), were employed to classify the GBV data in Somalia. The logistic regression (LR), decision tree (CART), random forest (RF), naïve Bayes (NB), k-nearest Neighbors (KNN), and support vector machine (SVM) methods were trained and evaluated. In addition, oversampling techniques were employed for improving the imbalanced datasets. Receiver operating characteristic curve (ROC) and the area under the curve (AUC) were used to assess each machine learning classifier and to compare performance on the original GBV dataset. Among the resampling techniques, SMOTE (RF = 0.992, CART = 0.969, and KNN = 0.957) outperformed ADASYN (RF = 0.912, CART = 0.910, and KNN = 0.876) and ROS (RF = 0.920, CART = 0.919, and KNN = 0.880) across almost all evaluation metrics. The classifiers that performed the best were random forest (RF) and classification and regression trees (CART), then k-nearest Neighbors. After resampling the imbalanced dataset, we may therefore conclude that the random forest (AUC = 0.972), CART (AUC = 0.969) and KNN (AUC = 0.957) machine learning classifiers are better at accurately classifying the k-nearest Neighbors dataset. In addition, compared to the other oversampling techniques, SMOTE was used to the machine learning classifiers to balance the imbalanced class distributions in favour of the minority class. In addition, SMOTE with the Mathews correlation coefficient (MCC) outperformed ADASYN and ROS resampling techniques. The MCC values for SMOTE reached their highest values (RF = 0.86, CART = 0.85, and KNN = 0.80), indicating strong overall predictive reliability of the machine learning models. Therefore, the findings of this analysis will assist government and non-government organizations in making policy decisions to GBV risks.

Panic disorder is a serious anxiety disorder that can significantly impact an individual's mental health. If left undetected, this disorder can disrupt daily life, social relationships, and overall quality of life. Early detection and intervention are crucial for managing panic disorder and improving the well-being of those affected. Technology plays a pivotal role in facilitating early detection through data-driven approaches that employ algorithms to identify patterns of behavior or symptoms associated with panic disorder. Accurate classification of panic disorder is crucial for effective diagnosis and treatment. However, machine learning models trained on imbalanced datasets, such as those containing panic disorder patients, are prone to overfitting, leading to poor generalization performance. This study investigates the effectiveness of the Synthetic Minority Oversampling Technique (SMOTE) in addressing overfitting in panic disorder dataset classification using the Random Forest algorithm. The results demonstrate that SMOTE significantly improves the classification performance of Random Forest. By mitigating overfitting and improving generalization to unseen data, SMOTE increases accuracy by 15 percentage points. Before using SMOTE, the accuracy was 82%, and after using SMOTE it is 97%. The findings underscore the promise of SMOTE as a tool for boosting the performance of machine learning algorithms in classifying panic disorder from imbalanced data.