Learning-based Classification Research Articles

Cataract Eye Disease Diagnosis Using the Random Forest Method

This study developed a machine learning-based classification model using the Random Forest algorithm to detect cataract risk based on 11 variables: age, gender, family history, lens opacity, visual acuity reduction, light sensitivity, color changes, double vision, intraocular pressure, slit-lamp results, and visual acuity. Feature importance analysis revealed that lens opacity and visual acuity variables contributed most significantly to cataract risk prediction, followed by intraocular pressure and visual acuity reduction. The system was designed using Google Colab for model training and Streamlit as an interactive interface, enabling real-time predictions with intuitive result visualization. After optimization using Grid Search, the model achieved an accuracy of 92.0%, precision of 95.0%, sensitivity of 90.0%, F1 Score of 92.4%, and specificity of 98.0%. This system is expected to serve as an effective supporting tool for medical professionals in the early diagnosis of cataracts.

Automated Recommendation of Extracting Local Variable Refactorings

Extracting local variable refactoring is frequently employed to replace one or more occurrences of a complex expression with simple accesses to a newly introduced variable. To facilitate the refactoring, most IDEs can automate the extract local variable refactorings when the to-be-extracted expressions are selected by developers. However, refactoring tools usually replace all expressions that are lexically identical to the selected one without a comprehensive analysis of the safety of the refactoring. The automatically conducted refactorings may lead to serious software defects. Besides that, existing refactoring tools rely heavily on software developers to spot to-be-extracted expressions although it is often challenging for inexperienced developers and maintainers to make the selection. To this end, in this paper, we propose an automated approach, called ValExtractor+ , to recommending extract local variable refactoring opportunities and to automatically and safely conduct the refactorings. ValExtractor+ is composed of two parts, i.e., solutionAdvisor and opportunityAdvisor. Given a to-be-extracted expression, solutionAdvisor leverages lightweight static source code analysis to validate potential side effects of the expression, and to identify expressions that could be extracted together with the selected expression as a single variable without changing the semantics of the program or introducing any new exceptions. The static code analysis significantly improves the safety in automated extraction of local variables. To free programmers from manually selecting to-be-extracted expressions, opportunityAdvisor leverages solutionAdvisor to automatically retrieve all expressions that could be extracted safely as well as their refactoring solutions. It then leverages a learning-based classifier to predict which of the retrieved expressions should be extracted. Evaluations on open-source applications suggest that solutionAdvisor successfully avoided all defects (more than two hundred) caused by extracting local variable refactorings conducted by Eclipse (243 defects) or IntelliJ IDEA (263 defects). Additionally, opportunityAdvisor was able to effectively recommend expressions for extraction, achieving 307 true positives (TP) and 21,121 true negatives (TN). Four pull requests from our work (PR IDs: 66 , 333 , 439 , and 360 ) were successfully merged into the Eclipse community repository, showcasing the practical impact and robustness of our approach as recognized by the wider developer community.

Deep Learning-Based Classification of Pulmonary Diseases from Chest X-Ray Images

Deep Learning-Based Classification of Pulmonary Diseases from Chest X-Ray Images

HViTML: Hybrid vision transformer with machine learning-based classification model for glaucomatous eye

HViTML: Hybrid vision transformer with machine learning-based classification model for glaucomatous eye

Machine Learning Algorithms in Controlled Donation After Circulatory Death Under Normothermic Regional Perfusion: A Graft Survival Prediction Model.

Several scores have been developed to stratify the risk of graft loss in controlled donation after circulatory death (cDCD). However, their performance is unsatisfactory in the Spanish population, where most cDCD livers are recovered using normothermic regional perfusion (NRP). Consequently, we explored the role of different machine learning-based classifiers as predictive models for graft survival. A risk stratification score integrated with the model of end-stage liver disease score in a donor-recipient (D-R) matching system was developed. This retrospective multicenter cohort study used 539 D-R pairs of cDCD livers recovered with NRP, including 20 donor, recipient, and NRP variables. The following machine learning-based classifiers were evaluated: logistic regression, ridge classifier, support vector classifier, multilayer perceptron, and random forest. The endpoints were the 3- and 12-mo graft survival rates. A 3- and 12-mo risk score was developed using the best model obtained. Logistic regression yielded the best performance at 3 mo (area under the receiver operating characteristic curve = 0.82) and 12 mo (area under the receiver operating characteristic curve = 0.83). A D-R matching system was proposed on the basis of the current model of end-stage liver disease score and cDCD-NRP risk score. The satisfactory performance of the proposed score within the study population suggests a significant potential to support liver allocation in cDCD-NRP grafts. External validation is challenging, but this methodology may be explored in other regions.

Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience

Multiple artificial intelligence systems have been created to facilitate accurate and prompt histopathological diagnosis of tumors using hematoxylin-eosin-stained slides. We aimed to investigate whether weakly supervised deep learning can aid in glioma diagnosis. We analyzed 472 whole slide images (WSIs) from 226 patients in West China Hospital (WCH) and 1604 WSIs from 880 patients in The Cancer Genome Atlas (TCGA). We utilized the OpenSlide library to load WSIs, segmented them into small patches using the DeepZoom module, and then normalized the color using the Reinhard method. A weakly supervised deep learning model was developed using ResNet-50 combined with an attention mechanism. We investigated the performance of the model by calculating area under the curve (AUC) in a ten-fold cross-validation setting. Heatmap visualizations showed the prediction mechanism of the model. The results were promising, with high AUC values for differentiating grades of astrocytomas, oligodendrogliomas, all gliomas, and glioma types in the TCGA dataset (0.9419, 0.8659, 0.9904, and 0.9298, respectively), and in the WCH cohort (0.9048, 0.7423, 0.9510, and 0.7098, respectively). The model demonstrated a strong ability to infer IDH status in the TCGA dataset (AUC = 0.9488). The weakly supervised deep learning model proved to be an effective and reliable tool for neuropathological diagnosis, making it an attractive auxiliary tool.

Machine learning-based classification and prediction of typical Chinese green tea taste profiles

Machine learning-based classification and prediction of typical Chinese green tea taste profiles

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning-based classification of medication adherence among patients with noncommunicable diseases

Machine learning-based classification of medication adherence among patients with noncommunicable diseases

Deep Learning-assisted Diagnosis of Extrahepatic Common Bile Duct Obstruction Using MRCP Imaging and Clinical Parameters.

Extrahepatic Common Bile Duct Obstruction (EHBDO) is a serious condition that requires accurate diagnosis for effective treatment. Magnetic Resonance Cholangiopancreatography (MRCP) is a widely used noninvasive imaging technique for visualizing bile ducts, but its interpretation can be complex. This study aimed to develop a deep learning-based classification model that integrates MRCP images and clinical parameters to assist radiologists in diagnosing EHBDO more accurately. A total of 465 patients with clinical data were included, of whom 143 also had MRCP images. Missing clinical values were addressed through data imputation. Object detection techniques were used to isolate the common bile duct region in the MRCP images. A multimodal deep learning fusion model was developed by combining the extracted imaging features with selected clinical parameters. To account for the varying significance of different features, a weighted loss function was applied. The performance of the fusion model was compared to that of single-modality approaches (using only MRCP images or clinical data), specifically the accuracy, sensitivity, specificity, and Area Under The Curve (AUC). The performance of the proposed deep learning fusion model was superior to that of models using only MRCP images or clinical parameters. The fusion model achieved an accuracy of 89.8%, AUC of 90.4%, sensitivity of 81.8%, and specificity of 95.7% in diagnosing EHBDO. By integrating MRCP imaging data and clinical parameters, the proposed deep learning model significantly enhanced the accuracy of EHBDO diagnosis. This proposed multimodal approach outperformed traditional single-modality methods, presenting a valuable tool for improving the diagnostic accuracy of bile duct obstruction.

Deep Learning-based Classification of Autism Spectrum Disorder Using Resting State fMRI Data

Deep Learning-based Classification of Autism Spectrum Disorder Using Resting State fMRI Data

Sentiment Analysis of Product Reviews Using Machine Learning and Pre-Trained LLM

Sentiment analysis via artificial intelligence, i.e., machine learning and large language models (LLMs), is a pivotal tool that classifies sentiments within texts as positive, negative, or neutral. It enables computers to automatically detect and interpret emotions from textual data, covering a spectrum of feelings without direct human intervention. Sentiment analysis is integral to marketing research, helping to gauge consumer emotions and opinions across various sectors. Its applications span analyzing movie reviews, monitoring social media, evaluating product feedback, assessing employee sentiments, and identifying hate speech. This study explores the application of both traditional machine learning and pre-trained LLMs for automated sentiment analysis of customer product reviews. The motivation behind this work lies in the demand for more nuanced understanding of consumer sentiments that can drive data-informed business decisions. In this research, we applied machine learning-based classifiers, i.e., Random Forest, Naive Bayes, and Support Vector Machine, alongside the GPT-4 model to benchmark their effectiveness for sentiment analysis. Traditional models show better results and efficiency in processing short, concise text, with SVM in classifying sentiment of short length comments. However, GPT-4 showed better results with more detailed texts, capturing subtle sentiments with higher precision, recall, and F1 scores to uniquely identify mixed sentiments not found in the simpler models. Conclusively, this study shows that LLMs outperform traditional models in context-rich sentiment analysis by not only providing accurate sentiment classification but also insightful explanations. These results enable LLMs to provide a superior tool for customer-centric businesses, which helps actionable insights to be derived from any textual data.

Machine Learning Methods in IoT Based Embedded Systems for Classifying Physical Faults in Water Distribution Networks

Water is the most important factor for the survival of living things on Earth. Although 70% of the Earth is water, the amount of drinkable water is approximately 0.3%. Therefore, creating a sustainable water policy and carrying out studies are very important for our world and our future. Most of the potable water resources are physical losses. In the evaluations made based on metropolitan municipalities, it was seen that the water loss rate was approximately 50%. The study aims to find water pipe faults using IoT (Internet of Things) based machine learning classifiers to prevent physical losses in water distribution networks. Within the scope of this study, an experimental environment was created and an IMU (Inertial Measurement Unit) sensor was fixed on plastic pipes of different diameters and lengths. Vibration data collected in different scenarios (pressure, etc. factors) were transferred to the ThingSpeak platform over the internet. The transferred data could be monitored in real-time on a server. Physical damage in the pipes was detected using signal pre-processing, feature extraction, and feature selection algorithms on vibration data. In the study, damages were classified using machine learning-based classification (Decision Trees, k-Nearest Neighbors, Linear Discriminant, Support Vector Machines) methods to predict the type of damage (solid, hole, multi-hole). The data set revealed within the scope of the study is thought to lead to scientific studies in this field. The results obtained are close to the state-of-the-art results.

Machine Learning-based Classification of Developing Countries and Exploration of Country-Specific ODA Strategies

Introduction: This study aimed to develop a systematic methodology for classifying recipient countries using machine learning, with the premise that tailoring mid- to long-term ODA strategies to country characteristics is essential. Additionally, it sought to propose ODA policy directions considering the unique attributes of classified developing countries. Methods: The research analyzed 166 countries, including both developed and developing nations, using SDG scores and GDP per capita as key indicators. Machine learning techniques, specifically neural network analysis and decision tree analysis, were employed for classification. Results: The analysis resulted in the classification of the 166 countries into 12 distinct groups, with seven nodes representing developing countries. Each group exhibited unique characteristics that informed the development of country-specific ODA strategies Conclusions: This study successfully developed a systematic classification methodology for recipient countries using machine learning. The resulting classification and proposed ODA strategies for each group provide a foundation for more targeted and effective ODA policies. This approach enables policymakers to tailor their strategies to the specific needs and characteristics of different developing country groups, potentially improving the impact and efficiency of ODA efforts.

Advanced classification of drug-drug interactions for assessing adverse effect risks of Fluvoxamine and Curcumin using deep learning in COVID-19

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.

Multi-Class Classification of Weed Images using Deep Learning Techniques

Identifying weed species plays a key role in agricultural management, helping to target eradication and precision farming. In this study, ResNet, VggNet and DenseNet were used to investigate the robust models capable of accurately discriminating between different weed species. The dataset used in this study consists of high resolution images of different weed species under different environmental conditions. Experimental results demonstrated the effectiveness of the proposed deep learning models in accurately identifying multiple weed species simultaneously. Evaluation metrics including accuracy, precision, recall and confusion matrix demonstrate the effectiveness of the models in discriminating between weed species. Experimental results indicate that among the convolutional neural network architectures used in this study, VggNet has the highest CA at 99.21%. This study discusses the implications of these results in the context of agricultural applications, highlighting the potential for scalable and efficient weed species identification and management through deep learning-based classification systems.

Genome-Wide DNA Methylation Identifies Potential Disease-Specific Biomarkers and Pathophysiologic Mechanisms in Irritable Bowel Syndrome, Inflammatory Bowel Disease, and Celiac Disease.

Irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), and celiac disease (CeD) present with similar gastrointestinal (GI) symptoms. DNA methylation-based biomarkers have not been investigated as diagnostic biomarkers to classify these disorders. We aimed to study DNA methylation profiles of IBS, IBD, CeD, and healthy controls (HC), develop machine learning-based classifiers, and identify associated gene ontology (GO) terms. Genome-wide DNA methylation of peripheral blood mononuclear cells from 315 patients with IBS, IBD, CeD, and HC was measured using Illumina's 450K or EPIC arrays. A methylation dataset on 304 IBD and HC samples was used for external validation. Differential methylation was measured using general linear models. Classifiers were developed using penalized generalized linear models using double cross-validation controlling for confounders. Functional enrichment was assessed using GO. Three hundred and fifteen participants (148 IBS, 47 IBD, 34 CeD, and 86 HC) had DNA methylation data. IBS-IBD and IBD-CeD showed the highest number of differentially methylated CpG sites followed by IBD-HC, CeD-HC, and IBS-HC. IBS-associated genes were enriched in cell adhesion and neuronal pathways, while IBD- and CeD-associated markers were enriched in inflammation and MHC class II pathways, respectively (p < 0.05). Classification performances assessed using area under the receiver operating characteristic curves (AUC) for IBS-IBD, IBS-CeD, and IBD-CeD were 0.80 (95% CI = 0.7-0.87, p = 6.75E-10), 0.78 (95% CI = 0.68-0.86, p = 4.57E-10), and 0.73 (95% CI = 0.62-0.83, p = 0.03), respectively. The performance of IBD-HC was successfully validated using external data (AUC = 0.74 [95% CI = 68-0.80, p < 0.001]). Blood-based DNA methylation biomarkers can potentially distinguish chronic GI disorders that present with similar symptoms. GO suggested functional significance of the classifiers in disease-specific pathology.

Deep Learning-Based Detection of Malignant Bile Duct Stenosis in Fluoroscopy Images of Endoscopic Retrograde Cholangiopancreatography.

The accurate distinction between benign and malignant biliary strictures (BS) poses a significant challenge. Despite the use of bile duct biopsies and brush cytology via endoscopic retrograde cholangiopancreaticography (ERCP), the results remain suboptimal. Single-operator cholangioscopy can enhance the diagnostic yield in BS, but its limited availability and high costs are substantial barriers. Convolutional Neural Network (CNN)-based systems may improve the diagnostic process and enhance reproducibility. Therefore, we assessed the feasibility of using deep learning to differentiate BS using fluoroscopy images during ERCP. We conducted a retrospective review of adult patients (n=251) from three university centers in Germany (Leipzig, Dresden, Halle) who underwent ERCP. We developed and evaluated a deep learning-based model using fluoroscopy images. The performance of the classifier was evaluated by measuring the area under the receiver operating characteristic curve (AUROC) and we utilized saliency map analyses to understand the decision-making process of the model. In cross-validation experiments, malignant BS were detected with a mean AUROC of 0.89 ± 0.03. The test set of the Leipzig cohort demonstrated an AUROC of 0.90. In two independent external validation cohorts (Dresden, Halle), the deep learning-based classifier achieved an AUROC of 0.72 and 0.76, respectively. The artificial intelligence model's predictions identified plausible characteristics within the fluoroscopy images. By using a deep learning model, we were able to discriminate malignant BS from benign biliary conditions. The application of artificial intelligence enhances the diagnostic yield of malignant BS and should be validated in a prospective design.

Deep Learning-Based Classification of Skin Lesions: Enhancing Melanoma Detection through Automated Preprocessing and Data Augmentation

Skin cancer of the most dangerous type, melanoma, requires an early and accurate diagnosis for its treatment to reduce mortality and increase the number of positive outcomes. Even with the availability of better imaging and diagnostic techniques, it is still difficult to differentiate between benign lesions and malignant melanoma because of overlapping features, noisy images and images with artefacts such as hair and glare. To overcome these challenges, this research adopts deep learning models to classify skin lesions based on images from the ISIC Archive dataset. The study establishes a strong two-stage classification framework. Therefore, noise reduction, ROI cutting, and data enhancement techniques are used for data pre-processing. Second, lesion classification is performed using a ResNet-based convolutional neural network (CNN) architecture. The model is trained and validated on a balanced dataset that contains an equal number of benign and malignant lesion categories. Using accuracy, precision, recall, F1 score and AUC, the system can be assessed and compared to other state-of-art approaches. The findings show that the proposed model has a high level of classification performance and a high level of discriminative ability between melanoma and benign lesions. The ROC curve effectively exhibits the model’s performance and accuracy, and the confusion matrix reveals tendencies to misclassify and where it should be improved. The application of sophisticated preprocessing methods improves model performance, responding to the issues arising from the presence of noise in data. This research is valuable to the field of dermatological diagnostics as it offers a scalable, automated means of skin lesion classification. The proposed framework can be applied clinically to assist dermatologists in the detection of early melanoma and, therefore benefit patients. Subsequent studies will address the development of combined approaches and the improvement of interpretability aids in order to increase the diagnostic accuracy and practical applicability of the methods in clinical practice.

Deep Learning-Based Classification of Rice Leaf Diseases Using Hybrid Ensemble Models

This research aimed to identify the possibility of applying deep learning to rice leaf diseases diagnostic procedures. The study applied different data preprocessing techniques that were the key to getting the data ready for the analysis process to a set of commonly used rice leaf images. The first process implemented was training different kinds of deep learning models, such as ResNet and DenseNet architectures, on the preprocessed data. To ensure the best possible performance, the top single models from each set were then combined into all the ensemble methods in a way that made them the most potent. The pre-trained models used were ResNet152V2, DenseNet121, InceptionResNetV2, MobilityNetv2, and all models were trained on the preprocessed data. The algorithms' success in classification was assessed based on the measures of accuracy, precision, recall, and F1 score. The most successful classifiers were combined into hybrid ensembles using the Ensemble method. The holistic approach to this matter brought the desired result that is a 98% accuracy in the classification of rice leaf diseases, which is truly a remarkable result that no single model can bring.