Applying Machine Learning Sampling Techniques to Address Data Imbalance in a Chilean COVID-19 Symptoms and Comorbidities Dataset

Applied Sciences, Год журнала: 2025, Номер 15(3), С. 1132 - 1132

Опубликована: Янв. 23, 2025

Reliably detecting COVID-19 is critical for diagnosis and disease control. However, imbalanced data in medical datasets pose significant challenges machine learning models, leading to bias poor generalization. The dataset obtained from the EPIVIGILA system Chilean Epidemiological Surveillance Process contains information on over 6,000,000 patients, but, like many current datasets, it suffers class imbalance. To address this issue, we applied various algorithms, both with without sampling methods, compared them using different classification diagnostic metrics such as precision, sensitivity, specificity, likelihood ratio positive, odds ratio. Our results showed that applying methods improved metric values contributed models better Effectively managing crucial reliable diagnosis. This study enhances understanding of how techniques can improve reliability contribute patient outcomes.

Язык: Английский

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Design of stacked ensemble classifier for skin cancer detection

Multimedia Tools and Applications, Год журнала: 2025, Номер unknown

Опубликована: Янв. 24, 2025

Язык: Английский

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Challenging the status quo: Why artificial intelligence models must go beyond accuracy in cervical cancer diagnosis

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 105, С. 107620 - 107620

Опубликована: Фев. 3, 2025

Язык: Английский

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Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review

Diagnostics, Год журнала: 2024, Номер 14(4), С. 454 - 454

Опубликована: Фев. 19, 2024

In recent years, there has been growing interest in the use of computer-assisted technology for early detection skin cancer through analysis dermatoscopic images. However, accuracy illustrated behind state-of-the-art approaches depends on several factors, such as quality images and interpretation results by medical experts. This systematic review aims to critically assess efficacy challenges this research field order explain usability limitations highlight potential future lines work scientific clinical community. study, was carried out over 45 contemporary studies extracted from databases Web Science Scopus. Several computer vision techniques related image video processing diagnosis were identified. context, focus process included algorithms employed, result accuracy, validation metrics. Thus, yielded significant advancements using deep learning machine algorithms. Lastly, establishes a foundation research, highlighting contributions opportunities improve effectiveness learning.

Язык: Английский

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Cloud and IoT based Smart Agent-driven Simulation of Human Gait for Detecting Muscles Disorder

Heliyon, Год журнала: 2025, Номер 11(2), С. e42119 - e42119

Опубликована: Янв. 1, 2025

Motion disorders affect a significant portion of the global population. While some symptoms can be managed with medications, these treatments often impact all muscles uniformly, not just affected ones, leading to potential side effects including involuntary movements, confusion, and decreased short-term memory. Currently, there is no dedicated application for differentiating healthy from abnormal ones. Existing analysis applications, designed other purposes, lack essential software engineering features such as user-friendly interface, infrastructure independence, usability learning ability, cloud computing capabilities, AI-based assistance. This research proposes computer-based methodology analyze human motion differentiate between unhealthy muscles. First, an IoT-based approach proposed digitize using smartphones instead hardly accessible wearable sensors markers. The data then simulated neuromusculoskeletal system. An agent-driven modeling method ensures naturalness, accuracy, interpretability simulation, incorporating neuromuscular details Henneman's size principle, action potentials, motor units, biomechanical principles. results are provided medical clinical experts aid in further investigation. Additionally, deep learning-based ensemble framework assist simulation results, offering both accuracy interpretability. A graphical interface enhances application's usability. Being fully cloud-based, infrastructure-independent accessed on smartphones, PCs, devices without installation. strategy only addresses current challenges treating but also paves way simulations by considering scientific computational requirements.

Язык: Английский

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Surgical Site Infections in Colorectal Cancer Surgeries: A Systematic Review and Meta-Analysis of the Impact of Surgical Approach and Associated Risk Factors

Life, Год журнала: 2024, Номер 14(7), С. 850 - 850

Опубликована: Июль 5, 2024

Background: Surgical site infections (SSIs) represent a noteworthy contributor to both morbidity and mortality in the context of patients who undergo colorectal surgery. Several risk factors have been identified; however, their relative significance remains uncertain. Methods: We conducted meta-analysis observational studies from inception up until 2023 that investigated for SSIs A random-effects model was used pool data calculate odds ratio (OR) 95% confidence interval (CI) each factor. Results: Our analysis included 26 with total 61,426 patients. The pooled results showed male sex (OR = 1.45), body mass index (BMI) ≥ 25 kg/m2 1.09), American Society Anesthesiologists (ASA) score 3 1.69), were all independent Conversely, laparoscopic surgery 0.70) found be protective Conclusions: revealed various factors, modifiable non-modifiable, associated surgical These findings emphasize targeted interventions, including optimizing glycemic control, minimizing blood loss, using techniques whenever feasible order decrease occurrence this particular group

Язык: Английский

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Enhanced gastric cancer classification and quantification interpretable framework using digital histopathology images

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Сен. 28, 2024

Язык: Английский

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Gastric Cancer Detection with Ensemble Learning on Digital Pathology: Use Case of Gastric Cancer on GasHisSDB Dataset

Diagnostics, Год журнала: 2024, Номер 14(16), С. 1746 - 1746

Опубликована: Авг. 12, 2024

Gastric cancer has become a serious worldwide health concern, emphasizing the crucial importance of early diagnosis measures to improve patient outcomes. While traditional histological image analysis is regarded as clinical gold standard, it labour intensive and manual. In recognition this problem, there been rise in interest use computer-aided diagnostic tools help pathologists with their efforts. particular, deep learning (DL) emerged promising solution sector. However, current DL models are still restricted ability extract extensive visual characteristics for correct categorization. To address limitation, study proposes ensemble models, which incorporate capabilities several deep-learning architectures aggregate knowledge many classification performance, allowing more accurate efficient gastric detection. determine how well these proposed performed, compared them other works, all were based on Histopathology Sub-Size Images Database, publicly available dataset cancer. This research demonstrates that achieved high detection accuracy across sub-databases, an average exceeding 99%. Specifically, ResNet50, VGGNet, ResNet34 performed better than EfficientNet VitNet. For 80 × 80-pixel sub-database, exhibited approximately 93%, VGGNet 94%, model excelled 120 120-pixel showed 99% accuracy, 97%, ResNet50 97%. 160 160-pixel again 98%, 92%, highlighting model’s superior performance resolutions. Overall, consistently provided three sub-pixel categories. These findings show may successfully detect critical from smaller patches achieve performance. The will diagnose using histopathological images, leading earlier identification higher survival rates.

Язык: Английский

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Next-Generation Diagnostics: The Impact of Synthetic Data Generation on the Detection of Breast Cancer from Ultrasound Imaging

Mathematics, Год журнала: 2024, Номер 12(18), С. 2808 - 2808

Опубликована: Сен. 11, 2024

Breast cancer is one of the most lethal and widespread diseases affecting women worldwide. As a result, it necessary to diagnose breast accurately efficiently utilizing cost-effective widely used methods. In this research, we demonstrated that synthetically created high-quality ultrasound data outperformed conventional augmentation strategies for diagnosing using deep learning. We trained deep-learning model EfficientNet-B7 architecture large dataset 3186 images acquired from multiple publicly available sources, as well 10,000 generated generative adversarial networks (StyleGAN3). The was five-fold cross-validation techniques validated four metrics: accuracy, recall, precision, F1 score measure. results showed integrating produced into training set increased classification accuracy 88.72% 92.01% based on score, demonstrating power models expand improve quality datasets in medical-imaging applications. This larger comprising synthetic significantly improved its performance by more than 3% over genuine with common augmentation. Various procedures were also investigated set’s diversity representativeness. research emphasizes relevance modern artificial intelligence machine-learning technologies medical imaging providing an effective strategy categorizing images, which may lead diagnostic optimal treatment options. proposed are highly promising have strong potential future clinical application diagnosis cancer.

Язык: Английский

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Transformative Advances in AI for Precise Cancer Detection: A Comprehensive Review of Non-Invasive Techniques

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 11, 2025

Язык: Английский

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