Synthetic Minority Oversampling Technique Research Articles

Metaheuristic-driven space partitioning and ensemble learning for imbalanced classification

Imbalanced classification is a common issue in Machine Learning, particularly when misclassifying minor instances leads to significant costs. In literature, various strategies have been employed to address this problem. These include data-level, algorithm-level, cost-sensitive, and hybrid-level algorithms designed to tackle imbalanced problems. This paper aims to introduce a novel method that simultaneously enhances the ability of classification models to identify patterns more effectively and addresses imbalanced problems while minimizing alterations to the original data distribution. Our proposed framework combines ensemble learning, space partitioning, and the Synthetic Minority Oversampling Technique (SMOTE). This method decomposes the space into balanced sub-spaces and then trains an ensemble classifier based on these sub-spaces using a bagging approach. In the initial step, we develop a Space Partitioning by Metaheuristic algorithm (SPMH) to divide the space into multiple balanced subspaces. In the subsequent step, we present Imbalanced Classification by SPMH (ICSPMH) as a solution to imbalanced class problems. ICSPMH uses SPMH multiple times to divide the space into different sub-spaces, creating various sub-spaces each time. It then trains different classifiers for each portion of the space, creating an ensemble classifier through a bagging technique. To assess the performance of our proposed framework, we selected 44 well-known datasets for comparison with some state-of-the-art approaches. The results demonstrate that ICSPMH outperforms other competent methods and can potentially reduce the oversampling rate to zero. Additionally, an experiment indicated that the choice of metaheuristic algorithm in SPMH does not significantly impact the final performance. The paper also includes a correlation analysis between oversampling rate and final performance, revealing that the framework effectively eliminates imbalanced data problems with minimal changes to the original dataset. In summary, because ICSPMH applies fewer changes in data distribution and sets up local classifiers that improve classification performance, it looks like a promising method for classifying imbalanced datasets.

An optimized multi-layer ensemble model for airborne networks intrusion detection

In view of the characteristics of airborne networks, such as the traffic data of 1553B data bus and public networks, numerous redundant and irrelevant data, and fewer but more types of attack behaviors, a highly representative balanced data subset is generated by combining K-means with synthetic minority oversampling technique (SMOTE). A feature selection (FS) method of fast correlation-based filter combined with information gain (IG-FCBF) is proposed to filter the key characteristics with 90 % cumulative importance. Aiming at the problem that it is not easy for a single classifier to accurately classify various types of attacks, as well as the difficulty in obtaining the best prediction results by adjusting default and manual parameters, a multi-layer ensemble model based on Bayesian optimization tree-structured Parzen estimator (BO-TPE) is proposed for airborne networks intrusion detection, combining the advantages of supervised learning, stacking ensemble method and hyperparameter optimization (HPO). The experimental results show the superiority and effectiveness of the model, as well as its universality in Integrated Avionics Systems (IAS) and onboard public networks, which provides a new approach for airborne networks intrusion identification and protection.

A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI

Cervical cancer is a cancer that remains a significant global health challenge all over the world. Due to improper screening in the early stages, and healthcare disparities, a large number of women are suffering from this disease, and the mortality rate increases day by day. Hence, in these studies, we presented a precise approach utilizing six different machine learning models (decision tree, logistic regression, naïve bayes, random forest, k nearest neighbors, support vector machine), which can predict the early stage of cervical cancer by analysing 36 risk factor attributes of 858 individuals. In addition, two data balancing techniques—Synthetic Minority Oversampling Technique and Adaptive Synthetic Sampling—were used to mitigate the data imbalance issues. Furthermore, Chi-square and Least Absolute Shrinkage and Selection Operator are two distinct feature selection processes that have been applied to evaluate the feature rank, which are mostly correlated to identify the particular disease, and also integrate an explainable artificial intelligence technique, namely Shapley Additive Explanations, for clarifying the model outcome. The applied machine learning model outcome is evaluated by performance evaluation matrices, namely accuracy, sensitivity, specificity, precision, f1-score, false-positive rate and false-negative rate, and area under the Receiver operating characteristic curve score. The decision tree outperformed in Chi-square feature selection with outstanding accuracy with 97.60%, 98.73% sensitivity, 80% specificity, and 98.73% precision, respectively. During the data imbalance, DT performed 97% accuracy, 99.35% sensitivity, 69.23% specificity, and 97.45% precision. This research is focused on developing diagnostic frameworks with automated tools to improve the detection and management of cervical cancer, as well as on helping healthcare professionals deliver more efficient and personalized care to their patients.

Research on variety identification of common bean seeds based on hyperspectral and deep learning

Accurate, fast and non-destructive identification of varieties of common bean seeds is important for the cultivation and efficient utilization of common beans. This study is based on hyperspectral and deep learning to identify the varieties of common bean seeds non-destructively. In this study, the average spectrum of 3078 hyperspectral images from 500 varieties was obtained after image segmentation and sensitive region extraction, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to achieve the equilibrium of the samples of various varieties. A one-dimensional convolutional neural network model (IResCNN) incorporating Inception module and residual structure was proposed to identify seed varieties, and Support Vector Machine (SVM), K-Nearest Neighbor (KNN), VGG19, AlexNet, ResNet50 were established to compare the identification effect. After analyzing the effects of multiple spectral preprocessing methods on the model, the study selected Savitzky-Golay smoothing correction (SG) for spectral preprocessing and extracted 66 characteristic wavelengths using Successive Projections Algorithm (SPA) as inputs to the discriminative model. Ultimately, the IResCNN model achieved the highest accuracy of 93.06 % on the test set, indicating that hyperspectral technology can accurately identify bean varieties, and the study provides a correct method of thinking for the non-destructive classification of multi-species small-sample bean varieties.

COMPARATIVE ANALYSIS OF STATE-OF-THE-ART CLASSIFIERS FOR PARKINSON'S DISEASE DIAGNOSIS

Parkinson's disease (PD) presents a growing global health challenge, with early detection being crucial for effective management and treatment. This study seeks to develop an innovative machine learning (ML) framework for the early detection of PD by integrating advanced techniques for data preprocessing, dimensionality reduction, feature selection, and ensemble classification, aiming to significantly improve detection accuracy and timeliness. The research employs a robust ML pipeline, beginning with data preprocessing using mean imputation, standardization, min-max scaling, and SMOTE (Synthetic Minority Over-sampling Technique) to handle imbalanced data. Dimensionality reduction is achieved through Principal Component Analysis (PCA), while feature selection is performed using SelectKBest coupled with the ANOVA F-test to identify the most relevant features. Four ensemble methods—Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine (SVM)—are evaluated for classification. Among the classifiers tested, the Gradient Boosting model stands out with an impressive accuracy of 0.9487, demonstrating its superior performance in PD detection. Integrating multiple preprocessing, dimensionality reduction, and feature selection techniques proves essential in optimizing model performance, highlighting the importance of a multifaceted approach in handling complex datasets. This research introduces a comprehensive ML framework that combines multiple advanced techniques in a streamlined process, significantly improving the early detection of Parkinson's disease. Ensemble methods, combined with strategic feature selection and data balancing techniques, offer a novel approach that could be applied to other neurodegenerative disorders, expanding its potential impact beyond PD detection.

Classifying Legendary Pokémon with SF-Random Forest Algorithm

Here’s an improved version of the abstract with better articulation: Accurate classification of legendary Pokémon is essential due to their distinct characteristics compared to regular Pokémon, impacting various domains such as research, gaming, and strategy development. This study employs the SF-Random Forest algorithm, an advanced variant of Random Forest, designed to effectively handle data heterogeneity and complexity. The dataset comprises 800 Pokémon samples, including attributes like type, base stats (HP, Attack, Defense, etc.), and other relevant features. To address the inherent imbalance between legendary and non-legendary Pokémon, the data preprocessing phase includes outlier removal, handling of missing values, normalization through Min-Max Scaling, and class balancing using the SMOTE (Synthetic Minority Over-sampling Technique) method. The preprocessed data is then used to train the SF-Random Forest model, with performance evaluated using metrics such as accuracy, precision, recall, and F1-score. The results reveal that SF-Random Forest achieves perfect scores across all metrics, demonstrating 100% accuracy, precision, recall, and F1-score. This highlights the algorithm's superior ability to identify key features and manage data imbalance compared to traditional classification methods. The study underscores the efficiency and robustness of SF-Random Forest as a classification tool, paving the way for the development of more advanced classification systems applicable to various fields requiring complex pattern recognition.

X-ray Image Analysis for Dental Disease: A Deep Learning Approach Using EfficientNets

Dental cavities are a highly common persistent dental problem that impacts populations across different age groups on a global scale. It is crucial to get a dental issue diagnosed as early as possible and with as much accuracy as possible to treat it efficiently and prevent any related issues. If a dental infection is not treated, it will eventually grow and cause tooth loss. Dental X-ray images are crucial and beneficial in the diagnostic process of dental diseases for dentists. By applying Deep Learning (DL) techniques to dental X-ray images, dental experts can efficiently and precisely detect dental conditions, including dental cavities, fillings and implants. The objective of this research is to assess the performance of DL-based methods for dental disease detection via panoramic radiographs. In this study, we evaluated the performance of all of the EfficientNet variants (e.g., EfficientNets B0-B7) to determine which one is the most effective model for detecting dental disease. Moreover, we utilized the Borderline Synthetic Minority Oversampling Technique (SMOTE) to cope with the issue related to the minority classes contained in the dataset. To assess the efficacy of the model, various metrics are employed, including recall, accuracy, precision, loss, and F1-score. As a result, the performance of the EfficientNet-B5 model was superior to that of the other EfficientNet models. The EfficientNet-B5 model achieved the following values for its metrics: F1-score, accuracy, recall, AUC, and precision: 98.37%, 98.32%, 98.32%, 99.21%, and 98.32%, respectively. The accuracy rates for the EfficientNet-B0, EfficientNet-B1, EfficientNet-B2, EfficientNet-B3, EfficientNet-B4, EfficientNet-B6, and EfficientNet-B7, are 91.59%, 94.12%, 93.28%, 85.71%, 94.96%, 96.64% and 90.76%, respectively. The results indicated that the EfficientNet-B5 model performs better than other EfficientNet classifiers, which supports dental professionals significantly in the recognition of dental diseases.

Abstract B019: A machine learning approach to predicting smoking cessation outcomes among Spanish-speaking smokers who completed a culturally targeted intervention

Abstract Introduction: Among the Hispanic/Latinx population in the US, four of five leading causes of death are attributed to smoking. Few evidence-based interventions have been developed to improve smoking cessation for this population. Our team has demonstrated the efficacy of a culturally targeted, extended self-help intervention for Spanish-speaking smokers, estimating a smoking abstinence rate of 33% at 24 months, compared to 24% for usual care. Further efforts are needed to enhance this efficacious, low-cost, scalable intervention approach. Machine learning (ML) is one approach to increase understanding of the predictors of treatment outcomes and inform strategies to improve the intervention. Toward that goal, this secondary data analysis utilized the decision tree (DT) model to predict self-reported 7-day point prevalence abstinence at 18-month follow-up for participants receiving our intervention. Method: Data from participants who reported smoking status at an 18-month follow-up (N=332 of 714 enrolled, 36% abstinent) were randomly split (80:20) into training and test datasets based on smoking status. We entered demographics, psychosocial, and smoking variables collected at baseline as predictors in the model. In addition to handling missing values and normalizing numeric features, we employed the Synthetic Minority Over-sampling Technique (SMOTE) combined with Tomek links (SMOTE-Tomek) to improve inter-class separability. Recursive Feature Elimination (RFE) using DT was implemented to identify the most relevant predictors. The cross-validation (CV) pipeline incorporated preprocessing, feature selection, handling class-imbalance, over-sampling, and model training with a DT as the interpretable classifier. Hyperparameters were tuned using stratified K-fold (K=5) CV to optimize parameters using Grid search. F1 score was selected as the primary metric as it accounts for each class performance by combining the precision and recall metrics. Finally, the performance of the classifier was evaluated in 20% of the unseen dataset. Results: The DT classifier for the CV achieved the F1 score of 0.73 [95% CI 0.66, 0.77], revealing a reasonably good performance in identifying smokers at 18-months. RFE selected familism, age, affect, and confidence as the most relevant predictors of smoking status. Individuals who had lower familism scores (<138), combined with higher age (>50), were more likely to be smokers at 18 months. Individuals who had higher familism scores (>138) combined with higher negative affect (>42) were more likely to be smokers. Conclusion: This study provides the first step toward personalized care for smoking cessation among the Hispanic/Latinx population and demonstrates the potential of DT classifier to predict cessation outcomes among individuals who received and completed a culturally and linguistically targeted smoking cessation intervention. Our future analysis will compare this model with conventional statistical modeling approaches and other ML algorithms to identify the best-performing parsimonious model. Citation Format: Ranjita Poudel, K. Ruwani M. Fernando, Matthew B. Schabath, Steven K. Sutton, Thomas H. Brandon, Issam El Naqa, Vani N. Simmons. A machine learning approach to predicting smoking cessation outcomes among Spanish-speaking smokers who completed a culturally targeted intervention [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B019.

Enhancing Security and Performance in Vehicular Adhoc Networks: A Machine Learning Approach to Combat Adversarial Attacks

Integrating Machine Learning (ML) techniques into Vehicular Adhoc Networks (VANETs) provides promising features in autonomous driving and ITS applications. In this paper, DSRC data is used to evaluate the effectiveness of different ML models, including Naive Bayes, Random Forest, KNN, and Gradient Boosting, in normal and adversarial scenarios. Since the dataset is relatively imbalanced, the Synthetic Minority Over-sampling Technique (SMOTE) is employed for sampling, and defensive distillation for improving model resilience to adversarial perturbations. From the results, it is clear that models such as Gradient Boosting and Random Forest show high accuracy in both cases, thus showing the potential of using Machine Learning to improve VANET security and reliability when new threats appear. Through this research, the significance of the application of ML in the protection of vehicular communication in order to enhance both traffic safety and flow has been articulated.

Feature Engineering for Agile Requirement Management Using Semantic Analysis

Efficient management and prioritization of software requirements are critical challenges in agile projects, where requirements constantly evolve due to changing user needs, business goals, and regulatory updates. This paper explores the role of semantic feature extraction in enabling adaptive management strategies. Using the PROMISE Expanded Dataset and the Coquina Dataset, we employed TF-IDF weighted Word2Vec for advanced tokenization and feature extraction. Latent Dirichlet Allocation (LDA) was used to analyze how preprocessing steps like stop word removal impact topic representation, revealing that removing stop words improved topic specificity and coherence. To address class imbalance, Synthetic Minority Over-sampling Technique (SMOTE) was applied, enhancing the model's ability to handle underrepresented classes effectively. Principal Component Analysis (PCA) reduced the dimensionality of TF-IDF weighted Word2Vec embeddings from 100 features to 30, while Analysis of Variance (ANOVA) identified the most significant features for classification. The results obtained identified three features to have p-values below 0.05 as statistically significant, p-value = 0.0000605, p-value = 0.00000000469, and p-value = 0.0024. These extracted features could be used as input to training machine learning models for predicting and managing software requirements adaptively during agile development. With the reduction of ambiguities and sentiments of the user at the requirement phase, the development phase could be undertaken seamlessly with ease.

Synthetic minority oversampling and iterative fluorescence-suppression integrated algorithm for Raman spectrum pesticide detection system

Raman spectrum preprocessing method for automatic denoising and suppression of the fluorescent background. In this method, noise is reduced using wavelet transform, and a modified polynomial curve fitting method is implemented such that an algorithm can independently identify the optimal curve parameters for fluorescent background suppression. To address the problem of imbalanced datasets, the present study employed a synthetic minority oversampling technique to increase the volume of data in minority classes. This technique enables the prediction of pesticides that are otherwise difficult to detect, and the prediction accuracy is comparable to that of detection with large data volumes. The proposed convolutional neural network model was verified to accurately identify the type of single pesticides and composition of mixed pesticides. The prediction accuracy for mixed pesticides reached 99.1%.

Influence of Preprocessing Methods of Automated Milking Systems Data on Prediction of Mastitis with Machine Learning Models

Missing data and class imbalance hinder the accurate prediction of rare events such as dairy mastitis. Resampling and imputation are employed to handle these problems. These methods are often used arbitrarily, despite their profound impact on prediction due to changes caused to the data structure. We hypothesize that their use affects the performance of ML models fitted to automated milking systems (AMSs) data for mastitis prediction. We compare three imputations—simple imputer (SI), multiple imputer (MICE) and linear interpolation (LI)—and three resampling techniques: Synthetic Minority Oversampling Technique (SMOTE), Support Vector Machine SMOTE (SVMSMOTE) and SMOTE with Edited Nearest Neighbors (SMOTEEN). The classifiers were logistic regression (LR), multilayer perceptron (MLP), decision tree (DT) and random forest (RF). We evaluated them with various metrics and compared models with the kappa score. A complete case analysis fitted the RF (0.78) better than other models, for which SI performed best. The DT, RF, and MLP performed better with SVMSMOTE. The RF, DT and MLP had the overall best performance, contributed by imputation or resampling (SMOTE and SVMSMOTE). We recommend carefully selecting resampling and imputation techniques and comparing them with complete cases before deciding on the preprocessing approach used to test AMS data with ML models.

A hybrid‐ensemble model for software defect prediction for balanced and imbalanced datasets using AI‐based techniques with feature preservation: SMERKP‐XGB

AbstractMaintaining software quality is a significant challenge as the complexity of software is increasing with the rise of the software industry. Software defects are a primary concern in complex modules, and predicting them in the early stages of the software development life cycle (SDLC) is difficult. Previous techniques to address this issue have not been very promising. We have proposed “A hybrid ensemble model for software defect prediction using AI‐based techniques with feature preservation” to overcome this problem. We have used the National Aeronautics and Space Administration (NASA) dataset from the PROMISE repository for testing and validation. By applying exploratory data analysis (EDA), feature engineering, scaling, and standardization, we found that the dataset is imbalanced, which can negatively affect the model's performance. To address this, we have used the Synthetic Minority Oversampling (SMOTE) technique and the edited nearest neighbor (ENN) (SMOTE‐ENN). We have also used recursive feature elimination cross‐validation (RFE‐CV) with a pipeline to prevent data leaking in CV and kernel‐based principal component analysis (K‐PCA) to minimize dimensionality and selectively relevant features. The reduced dimensional data is then given to the eXtreme Gradient Boosting (XGBoost) for classification, resulting in the hybrid‐ensemble (SMERKP‐XGB) model. The proposed SMERKP‐XGB model is better than previously developed models in terms of accuracy (CM1: 97.53%, PC1: 92.05%, and PC2: 97.45%, KC1:95.65%), and area under the receiver operating characteristic curve values (CM1:96.30%, PC1:98.30%, and PC2:99.30%: KC1: 93.54) and other evaluation criteria mentioned in the literature.

An intelligent hybrid model for cyber attack classification with selected feature set

Cyber security evolving as a severe problem almost in all sectors of cyberspace, due to the time-to-time increase in the number of security breaches. Numerous Zero-days attacks occur continuously, due to the increase in multiple protocols. Almost all of these attacks are small variants of previously known cyber attacks. Moreover, even the advanced approach like Machine Learning (ML), faces the difficulty in identifying those attack’s small mutants over time. Recently, Deep Learning (DL) has been utilized for multiple applications related to cybersecurity fields. Making use of this DL to identify the cyber attack might be a resilient mechanism for novel attacks or tiny mutations. Thereby, a novel cyber attack classification model named DCNN-Bi-LSTM-ICS is proposed in this work. This proposed DCNN-Bi-LSTM-ICS has five working stages. Firstly, in the data acquisition stage, the input data (considering the datasets) for attack classification has been collected. These raw data are pre-processed in the second stage, where an improved class imbalance balancing processing is conducted which makes use of the Improved Synthetic Minority Oversampling Technique (ISMOTE). In the third stage, along with the conventional mutual information and statistical features, Improved holo-entropy-based features are extracted. To choose the appropriate feature from those retrieved features, an Improved Chi-Square (ICS) processing is developed in the fourth stage. In the final classification stage, a hybrid classification model that combines both the Deep Convolutional Neural Network (DCNN) and Bi-directional Long Short Term Memory (Bi-LSTM) has been developed. The outcomes show that the proposed DCNN-Bi-LSTM-ICS can offer outstanding performance in the cyber attack classification task.

Improved phase prediction of high-entropy alloys assisted by imbalance learning

Predicting phase formation is crucial in novel high-entropy alloys (HEAs) design. Herein, machine learning and imbalance learning algorithms were combined together to improve the phase prediction of HEAs. In this work, an extensive database by collecting experimental data from published literature was constructed, and the key features affecting the phase formation of HEAs were filtered out by performing a three-step feature selection process. Then, extreme gradient boosting (XGB) models were constructed to categorize phase structures of HEAs with high accuracies. Moreover, the Synthetic Minority Oversampling TEchnique (SMOTE) algorithm was employed for data oversampling to address the data imbalance issue. It was found that imbalanced learning significantly improves the phase prediction, particularly for the minority class, without costing the overall prediction accuracy. Finally, a machine learning-base protocol was proposed to integrate established models to classify the phase formation of HEAs into seven phase labels, and its generalization ability was verified. The present work provides a practical approach in predicting phase structures of HEAs and enhances the efficiency in developing advanced HEAs.

Applying deep learning-based ensemble model to [18F]-FDG-PET-radiomic features for differentiating benign from malignant parotid gland diseases.

To develop and identify machine learning (ML) models using pretreatment 2-deoxy-2-[18F]fluoro-D-glucose ([18F]-FDG)-positron emission tomography (PET)-based radiomic features to differentiate benign from malignant parotid gland diseases (PGDs). This retrospective study included 62 patients with 63 PGDs who underwent pretreatment [18F]-FDG-PET/computed tomography (CT). The lesions were assigned to the training (n = 44) and testing (n = 19) cohorts. In total, 49 [18F]-FDG-PET-based radiomic features were utilized to differentiate benign from malignant PGDs using five different conventional ML algorithmic models (random forest, neural network, k-nearest neighbors, logistic regression, and support vector machine) and the deep learning (DL)-based ensemble ML model. In the training cohort, each conventional ML model was constructed using the five most important features selected by the recursive feature elimination method with the tenfold cross-validation and synthetic minority oversampling technique. The DL-based ensemble ML model was constructed using the five most important features of the bagging and multilayer stacking methods. The area under the receiver operating characteristic curves (AUCs) and accuracies were used to compare predictive performances. In total, 24 benign and 39 malignant PGDs were identified. Metabolic tumor volume and four GLSZM features (GLSZM_ZSE, GLSZM_SZE, GLSZM_GLNU, and GLSZM_ZSNU) were the five most important radiomic features. All five features except GLSZM_SZE were significantly higher in malignant PGDs than in benign ones (each p < 0.05). The DL-based ensemble ML model had the best performing classifier in the training and testing cohorts (AUC = 1.000, accuracy = 1.000 vs AUC = 0.976, accuracy = 0.947). The DL-based ensemble ML model using [18F]-FDG-PET-based radiomic features can be useful for differentiating benign from malignant PGDs. The DL-based ensemble ML model using [18F]-FDG-PET-based radiomic features can overcome the previously reported limitation of [18F]-FDG-PET/CT scan for differentiating benign from malignant PGDs. The DL-based ensemble ML approach using [18F]-FDG-PET-based radiomic features can provide useful information for managing PGD.

Harnessing Machine Learning to Predict Methadone Overdose Risk: Insights from Illinois's SUDORS Data

Knowledge of decedent characteristics associated with methadone involvement in unintentional drug overdose deaths can help to inform efforts to intervene in these deaths. ML models with existing data, offer a cost-effective means to help create effective interventions. In this study, we aim to develop an optimized ML algorithm for predicting overdose death patterns due to methadone using data from Illinois's State Unintentional Drug Overdose Reporting System (SUDORS). We utilized IL SUDORS 2019-2022 data for training (n = 11931) with selected indicators (n = 28). To address the imbalance in non-methadone-related deaths, we applied the Synthetic Minority Oversampling Technique. Next, we assessed various machine learning models, including logistic regression, support vector machine, random forest, neural network, and ridge regression. Model performance was evaluated using metrics such as accuracy, precision, area under the precision-recall curve (AUPRC), etc. Utilizing separate training (n = 15813), validation (n = 3388), and test (n = 3389) datasets, the Random Forest Model outperforms all other models with 92% accuracy, 91% precision, 93% recall, and 0.97 AUPRC. Notably, these outcomes were achieved using primarily demographic indicators. Through analyzing partial dependence plots, we were able to see how changes in each indicator dynamically influence methadone-related fatalities. This study demonstrates the potential of ML models in identification of methadone involvement in unintentional drug overdose deaths and contributes to the knowledge base for the prevention of these deaths. Leveraging SUDORS data, the Random Forest Model exhibited exceptional performance, highlighting its value for healthcare professionals, prevention and harm reduction specialists, and policymakers.

Optimizing Prehospital Stroke Diagnosis: Integrating Machine Learning with the FAST Scoring System

Strokes are a leading cause of disability and death worldwide, and timely diagnosis is critical for effective treatment. Prehospital stroke diagnosis by emergency medical services (EMS) is essential for ensuring prompt care. The FAST (Face, Arms, Speech, and Time) scoring system is a common tool used to identify stroke symptoms quickly. However, the traditional FAST system has limitations, including reliance on subjective assessments and potential misdiagnoses due to symptom variability. This study aims to optimize the FAST scoring system and compare its performance with various machine learning models to enhance stroke prediction accuracy. A dataset from an ambulance service, including prehospital data such as age, sex, and FAST test results, was used. Data preprocessing involved handling missing values, encoding categorical variables, and applying the Synthetic Minority Over-sampling Technique (SMOTE) to balance the dataset. A grid search was conducted to test different weighting schemes for the FAST system. Machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, and Decision Tree, were trained and evaluated using cross-validation on the training set and tested on a separate test set. The traditional FAST system showed an accuracy of 62.95%. Optimizing the FAST system through grid search marginally increased accuracy to 64.36%. However, machine learning models, particularly Random Forest, significantly outperformed the FAST system, achieving a test set accuracy of 88.54%. XGBoost also demonstrated strong performance with an accuracy of 77.07%, while Logistic Regression, SVM, and Decision Tree showed lower accuracies. These findings suggest that integrating machine learning models into EMS protocols could substantially improve the accuracy of prehospital stroke diagnosis, leading to quicker and more accurate identification and treatment of stroke patients. Although the optimized FAST system offers slight improvements, machine learning models present a promising avenue for enhancing stroke prediction and improving patient outcomes in emergency settings while acknowledging practicality issues.

Employee attrition prediction with convolutional neural network and synthetic minority over-sampling technique

ABSTRACT Employee attrition negatively affects organizational efficiency, customer service quality, and brand reputation. In this study, we explore the important issue of predicting employee attrition, which aids managers in taking effective retention strategies and mitigating the significant costs associated with recruiting and training new personnel. Different from existing research, we introduce a novel deep learning model featuring an intermediary layer that automatically generates a hidden image representation from tabular data. This intermediary step facilitates the efficient utilization of Convolutional Neural Networks, specifically designed for image data, thereby enhancing predictive accuracy. Furthermore, we employ the widely used Synthetic Minority Over-sampling Technique for handling imbalanced data to further improve our model’s performance. Our new CNN-based deep learning model demonstrated the best performance for predicting employee attrition, which can assist in reducing costs related to turnover and facilitate the implementation of a succession plan to ensure seamless transitions.

Challenges and opportunities of generative models on tabular data

Tabular data, organized like tables with rows and columns, is widely used. Existing models for tabular data synthesis often face limitations related to data size or complexity. In contrast, deep generative models, a part of deep learning, demonstrate proficiency in handling large and complex data sets. While these models have shown remarkable success in generating image and audio data, their application in tabular data synthesis is relatively new, lacking a comprehensive comparison with existing methods. To fill this gap, this study aims to systematically evaluate and compare the performance of deep generative models with these existing methods for tabular data synthesis, while also investigating the efficacy of post-processing techniques. We aim to identify strengths and limitations and provide insights for future research and practical applications. Our study showed that the Synthetic Minority Oversampling Technique (SMOTE) and its variants outperform deep generative models, especially for small datasets. However, we observed that an ensemble of deep generative models and post-generation processing performs better on large datasets than SMOTE alone. The results of our study indicate that deep generative models hold promise as a valuable tool for generating tabular data. Nonetheless, further research is warranted to enhance the performance of deep generative models and gain a comprehensive understanding of their limitations.