Improve Patient Outcomes Research Articles

The PNS Nurse Program: A Health Care Support Concept for Patients With Immune-Mediated Peripheral Nervous System Diseases.

Immune-mediated peripheral nervous system (PNS) disorders pose diagnostic and therapeutic challenges, necessitating collaborative, patient-centered care. Limited access to specialized centers leads to delayed diagnosis and care, as seen during the COVID-19 pandemic. To address these challenges, accessible specialized care is crucial. On-site support plays a vital role in advising and assisting patients and caregivers, enabling multidisciplinary care for PNS diseases. The PNS Nurse Education Program tackles these complexities, using specialized nurses experienced in multiple sclerosis and Parkinson disease. Focusing on peripheral neuroimmunologic disorders, PNS nurses monitor disease severity, optimize communication, and provide therapeutic support in the recently started era of available immunotherapies. Collaboration with other healthcare sectors and support groups further enhances patient care. Ultimately, the PNS Nurse Education Program aims to bridge the gap between complex treatments and limited specialized care, improving patient outcomes and relieving burdens on patients, caregivers, and healthcare systems.

Oculomics: A Crusade Against the Four Horsemen of Chronic Disease.

Chronic, non-communicable diseases present a major barrier to living a long and healthy life. In many cases, early diagnosis can facilitate prevention, monitoring, and treatment efforts, improving patient outcomes. There is therefore a critical need to make screening techniques as accessible, unintimidating, and cost-effective as possible. The association between ocular biomarkers and systemic health and disease (oculomics) presents an attractive opportunity for detection of systemic diseases, as ophthalmic techniques are often relatively low-cost, fast, and non-invasive. In this review, we highlight the key associations between structural biomarkers in the eye and the four globally leading causes of morbidity and mortality: cardiovascular disease, cancer, neurodegenerative disease, and metabolic disease. We observe that neurodegenerative disease is a particularly promising target for oculomics, with biomarkers detected in multiple ocular structures. Cardiovascular disease biomarkers are present in the choroid, retinal vasculature, and retinal nerve fiber layer, and metabolic disease biomarkers are present in the eyelid, tear fluid, lens, and retinal vasculature. In contrast, only the tear fluid emerged as a promising ocular target for the detection of cancer. The retina is a rich source of oculomics data, the analysis of which has been enhanced by artificial intelligence-based tools. Although not all biomarkers are disease-specific, limiting their current diagnostic utility, future oculomics research will likely benefit from combining data from various structures to improve specificity, as well as active design, development, and optimization of instruments that target specific disease signatures, thus facilitating differential diagnoses.

Geriatric Oncology: A 5-Year Strategic Plan.

The increasing rate of the older adult population across the world over the next 20 years along with significant developments in the treatment of oncology will require a more granular understanding of the older adult population with cancer. The ASCO Geriatric Oncology Community of Practice (COP) herein provides an outline for the field along three fundamental pillars: education, research, and implementation, inspired by ASCO's 5-Year Strategic Plan. Fundamental to improving the understanding of geriatric oncology is research that intentionally includes older adults with clinically meaningful data supported by grants across all career stages. The increased knowledge base that is developed should be conveyed among health care providers through core competencies for trainees and continuing education for practicing oncologists. ASCO's infrastructure can serve as a resource for fellowship programs interested in acquiring geriatric oncology content and provide recommendations on developing training pathways for fellows interested in pursuing formalized training in geriatrics. Incorporating geriatric oncology into everyday practice is challenging as each clinical setting has unique operational workflows with barriers that limit implementation of valuable geriatric tools such as Geriatric Assessment. Partnerships among experts in quality improvement from the ASCO Geriatric Oncology COP, the Cancer and Aging Research Group, and ASCO's Quality Training Program can provide one such venue for implementation of geriatric oncology through a structured support mechanism. The field of geriatric oncology must continue to find innovative strategies using existing resources and partnerships to address the pressing needs of the older adult population with cancer to improve patient outcomes.

Implementation of a Global Pediatric Trauma Course in an Upper Middle–Income Country: A Pilot Study

IntroductionOver 90% of pediatric trauma deaths occur in low- and middle-income countries (LMICs), yet pediatric trauma-focused training remains unstandardized and inaccessible, especially in LMICs. In Brazil, where trauma is the leading cause of death for children over age 1, we piloted the first global adaptation of the Trauma Resuscitation in Kids (TRIK) course and assessed its feasibility. MethodsA 2-day simulation-based global TRIK course was hosted in Belo Horizonte in October 2022, led by one Brazilian and four Canadian instructors. The enrollment fee was $200 USD, and course registration sold out in 4 d. We administered a knowledge test before and after the course and a postcourse self-evaluation. We recorded each simulation to assess participants’ performance, reflected in a team performance score. Groups received numerical scores for these three areas, which were equally weighted to calculate a final performance score. The scores given by the two evaluators were then averaged. As groups performed the specific simulations in varying orders, the simulations were grouped into four time blocks for analysis of performance over time. Statistical analysis utilized a combination of descriptive analysis, Wilcoxon signed-rank tests, Kruskal–Wallis tests, and Wilcoxon rank-sum tests. ResultsTwenty-one surgeons (19 pediatric, one trauma, one general) representing four of five regions in Brazil consented to study participation. Women comprised 76% (16/21) of participants. Overall, participants scored higher on the knowledge assessment after the course (68% versus 76%; z = 3.046, P < 0.001). Participants reported improved knowledge for all tested components of trauma management (P < 0.001). The average simulation performance score increased from 66% on day 1% to 73% on day 2, although this increase was not statistically significant. All participants reported they were more confident managing pediatric trauma after the course and would recommend the course to others. ConclusionsCompletion of global TRIK improved surgeons’ confidence, knowledge, and clinical decision-making skills in managing pediatric trauma, suggesting a standardized course may improve pediatric trauma care and outcomes in LMICs. We plan to more closely address cost, language, and resource barriers to implementing protocolized trauma training in LMICs with the aim to improve patient outcomes and equity in trauma care globally.

An efficient synthetic minority oversampling technique-based ensemble learning model to detect COVID-19 severity

The COVID-19 pandemic has highlighted the importance of accurately predicting disease severity to ensure timely intervention and effective allocation of healthcare resources, which can ultimately improve patient outcomes. This study aims to develop an efficient machine learning (ML) model based on patient demographic and clinical data. It utilizes advanced feature engineering techniques to reduce the dimensionality of dataset and address the issue of highly imbalanced data using synthetic minority oversampling technique (SMOTE). The study employs several ensemble learning models, including XGBoost, Random Forest, AdaBoost, voting ensemble, enhanced-weighted voting ensemble, and stack-based ensembles with support vector machine (SVM) and Gaussian Naïve Bayes as meta-learners, to develop the proposed model. The results indicate that the proposed model outperformed the top-performing models reported in previous studies. It achieved an accuracy of 0.978, sensitivity of 1.0, precision of 0.875, F1-score of 0.934, and receiver operating characteristic area under the curve (ROC-AUC) of 0.965. The study identified several features that significantly correlated with COVID-19 severity, which included respiratory rate (breaths per minute), c-reactive proteins, age, and total leukocyte count (TLC) count. The proposed approach presents a promising method for accurate COVID-19 severity prediction, which may prove valuable in assisting healthcare providers in making informed decisions about patient care.

Implementation Paper on Dermatological Proriasis and other Skin Diseases Diagnosis Using Machine Learning

Abstract: This paper "Dermatological Psoriasis and Other Skin Diseases Diagnosis Using Machine Learning" aims to develop an efficient and accurate system for identifying psoriasis, a chronic skin condition, through the utilization of machine learning techniques. Psoriasis affects millions worldwide, presenting challenges in its diagnosis and treatment. Leveraging advanced algorithms, this project seeks to analyse various skin images and clinical data to create a robust model capable of distinguishing psoriatic lesions from other dermatological conditions with high precision. By employing machine learning algorithms such as convolutional neural networks (CNNs) and support vector machines (SVMs), alongside feature extraction methods, the system aims to automate and streamline the diagnostic process, potentially reducing both time and burdens on healthcare professionals. Ultimately, the implementation of such a tool could significantly enhance early detection rates, improve patient outcomes, and facilitate more targeted treatment strategies for individuals affected by psoriasis

Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images

Abstract: This paper pivots on (DR) is a leading cause of blindness worldwide. Early detection and effective management are crucial in preventing damage to the retina. In recent years, (DL) techniques have shown promise in aiding the DR. This paper aims to investigate the diagnosis of using (CNNs) for automated DR screening. The study utilized a available patient dataset of retinal images to train and testing CNN model for DR detection and classification. Results showed that given (DL) techniques achieved with accurately in both tasks, outperforming existing state-of-the-art methods. The findings suggest the (DL) algorithms can aid in the early detection and management of DR, potentially reducing the burden of the condition on healthcare systems and improving patient outcomes

Sepsis Prediction

Abstract: Sepsis manifests as a life-threatening condition wherein the body's response to infection triggers organ dysfunction. Typically, this immune response inadvertently damages the body's tissues while combating the infection. Within the overall range of sepsis, septic shock represents a critical subset characterized by notable circulatory, cellular, and metabolic abnormalities, resulting in a increased mortality rate than standard sepsis. Noteworthy is the consensus from various studies highlighting the crucial role of early intervention in enhancing the survival chances of sepsis patients. Given the urgency of timely recognition and treatment, there is a heightened focus on sepsis prediction. Traditional scoring systems such as APACHE, SAPS, and SOFA provide insights into disease severity and prognosis but lack the capacity for early sepsis detection. This study uses ML techniques and analyzes extensive patient data, which includes vital signs and medical history, to identify important indicators and prognostic patterns that find the occurrence of sepsis. By applying advanced algorithms like neural networks and ensemble methods, the model shows impressive accuracy in predicting the progression of sepsis. In addition, detailed analysis of trait significance helps to understand key prognostic markers, allowing clinicians to strategically prioritize interventions. Successful integration of this prognostic tool into the clinical setting offers a promising opportunity for preventive management of sepsis, which can reduce mortality and improve patient outcomes. We built a (LSTM) network to distinguish the internal association between different. indicators in clinical data. Analysis includes use of electronic health records, multicenter collaboration, and challenge development. Our goal is to support the research of accurate and timely sepsis predictors, ultimately improving patient outcomes.

Pioneering Dermatological Disease Detection with CNNs

Abstract: Skin cancer poses a significant public health concern globally, with its incidence steadily rising. Early and accurate detection plays a pivotal role in improving patient outcomes and survival rates. With leveraging advancements in deep learning, this project explores the efficacy of Convolutional Neural Networks (CNNs) in automating the classification and detection of skin cancer from dermatoscopic images. The methodology employed in this project involves the development and optimization of a CNN architecture tailored for skin cancer analysis. The proposed model harnesses the hierarchical feature learning capabilities of CNN to automatically extract intricate patterns and discriminative features from dermatoscopic images, enabling accurate differentiation among various skin lesion types, including melanoma, basal cell carcinoma, and benign nevi. In addition to classification, the developed CNN architecture integrates a lesion detection mechanism. This auxiliary functionality enables the identification and delineation of potentially cancerous regions within skin lesions. The proposed model’s detection capabilities are evaluated through bounding box prediction and segmentation masks, exhibit notable performance in localizing suspicious areas, aiding dermatologists in targeted examinations. With the help of this project, we can prevent the cause of damage to skin at an early stage.

Prediction of Abnormal Growth of Breast Cancer Cell Using Support Vector Machine

Abstract: Breast cancer is a serious health concern that affects millions of women worldwide. Early detection of breast cancer is crucial for effective treatment and improved survival rates. Artificial intelligence (AI) has shown great promise in the field of medical imaging and has been increasingly used for breast cancer detection. Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common cancer types. According to the National Breast Cancer foundation, in 2020 alone, more than 276,000 new cases of invasive breast cancer and more than 48,000 non-invasive cases were diagnosed in the US. To put these figures in perspective, 64% of these cases are diagnosed early in the disease’s cycle, giving patients a 99% chance of survival. In this paper we develop a system for diagnosis, prognosis and prediction of breast cancer using AI. This will assist the doctors in diagnosis of the disease. In this research work, we systematically reviewed previous work done on detection and treatment of breast cancer using genetic sequencing or histopathological imaging with the help of deep learning and machine learning We use of AI for breast cancer detection, including studies that have used mammography, ultrasound, and magnetic resonance imaging (MRI) as imaging modalities. AI can also improve the accuracy and efficiency of breast cancer screening and diagnosis, as well as the challenges that must be addressed to ensure the successful integration of AI into clinical practice. However, further research and development are needed to optimize AI algorithms and ensure their safety and effectiveness in clinical practice. Breast cancer detection has emerged as a critical area of research, leveraging the power of Artificial Intelligence (AI) to enhance diagnostic accuracy and efficiency. Utilizing AI technologies, particularly deep learning, enables the development of robust and precise models for early breast cancer detection. These models analyze mammographic images, identifying subtle patterns indicative of malignancies that may escape human eye detection. Transfer learning, employing pre-trained neural network architectures, has demonstrated significant success in leveraging existing knowledge for improved performance. The integration of convolutional neural networks (CNNs) and image processing techniques empowers AI models to discern subtle abnormalities, aiding in the timely identification of potential breast tumors. Additionally, AI-driven diagnostic tools can streamline radiologists' workflow, providing rapid and reliable assessments, ultimately contributing to early intervention and improved patient outcomes. As these technologies continue to evolve, the synergy between AI and breast cancer detection promises to revolutionize screening methods, fostering a paradigm shift towards more accurate, efficient, and accessible diagnostic practices in the realm of breast health.

Revolutionizing Disease Prediction with Deep Learning and Predictive Analysis

Abstract: The integration of deep learning and predictive analysis has emerged as a powerful tool in the healthcare improvement effort, revolutionizing the process of illness prediction. The purpose of this project is to develop a comprehensive framework that can precisely predict and identify a range of diseases at an early stage using these technologies. Deep learning algorithms are used to uncover complicated patterns and connections that may point to the onset of a disease by evaluating massive datasets that include patient demographics, medical history, genetic data, and environmental factors. The predictive capabilities of these algorithms enable healthcare practitioners to more precisely identify potential health issues, enabling preventive intervention and customized treatment regimens. Moreover, putting this idea into reality promises to improve patient outcomes, substantially reduce healthcare costs, and ultimately alter the landscape of preventative medicine.

Granulicatella adiacens: An uncommon pathogen in prosthetic joint infections

Granulicatella adiacens is an uncommon culprit in prosthetic joint infections, especially when predisposing factors are absent. Here, we present the case of a 78-year-old female who developed an acute surgical site infection shortly after undergoing left total knee arthroplasty. Despite initial negative cultures, subsequent analysis identified Granulicatella adiacens in the knee fluid, marking only the second reported instance in the United States and the seventh globally. This singular case highlights the considerable challenges associated with isolating this organism, emphasizing its atypical presentation and diagnostic elusiveness. Typically associated with endocarditis and bacteremia, its involvement in isolated surgical site infections is particularly unusual. Our case underscores the necessity for heightened clinical suspicion and the adoption of specialized diagnostic methodologies to accurately detect Granulicatella adiacens infections. Given its fastidious nature and nutritional requirements, standard culture methods may fail to identify this pathogen, necessitating alternative diagnostic approaches. As such, the utilization of specialized media becomes paramount in clinical diagnostics to facilitate appropriate management and optimize healthcare resource allocation. By enhancing our ability to detect and treat infections caused by Granulicatella adiacens, we can mitigate patient morbidity and reduce the burden of unnecessary interventions, thereby improving patient outcomes and healthcare efficiency.

A comprehensive study of machine learning-based methods to predict epileptic seizures

People with epilepsy have many difficulties as a result of this complicated brain condition, which is typified by frequent convulsions. Symptoms of these seizures include bizarre behaviors, odd sensations, and in extreme cases, loss of awareness. These seizures appear as episodes of aberrant electrical impulses in the central nervous system. Successful epilepsy management depends on early seizure detection and identification, which allows for appropriate intervention to minimize risks and improve patient outcomes. Two major reasons have contributed to the extraordinary advancements in the area of epilepsy investigations in the last few years: the explosive development of machine learning techniques and the decreasing cost of non-invasive electroencephalography (EEG) apparatus. The availability of low-cost EEG equipment has made it easier to gather information on brain activity, which has opened up new avenues for monitoring and analyzing episodes of epileptic seizures away from conventional medical settings. The abundance of data and the advancement of machine learning methods have created new opportunities for the early identification and forecasting of seizures. Machine learning algorithms can predict seizures based on EEG data, providing patients with epilepsy with more control and informed decision-making. This paper offers a current review of current methods for treating epileptic seizures. The feature extraction techniques and classification algorithms receive particular focus. The most popular EEG datasets and their accessibility are listed. The approaches that are examined range from those that use more established machine learning techniques, such as naive Bayes models, Support Vector Machines (SVM), and Linear Discriminant Analysis (LDA), to those that take advantage of more recent deep learning techniques, like (Long-Short Term Memory, or LSTM), and deep Convolutional-Neural-Networks (CNN).

Molecular basis of hereditary cardiomyopathy: A systematic review

Hereditary cardiomyopathies include a diverse spectrum of myocardial disorders characterized by mechanical and electrical abnormalities, among which hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM) are distinguished. This paper dives into the genetic foundations of these conditions, with a focus on mutations in genes encoding sarcomere proteins, thin filament proteins, and calcium homeostasis regulators. The familial aggregation of cardiomyopathies underscores the substantial genetic component, with autosomal dominant inheritance patterns predominant in some cases. Genetic testing and counseling emerge as pivotal tools for early diagnosis, risk assessment, and the formulation of personalized treatment strategies. The integration of genetic insights into clinical management holds promise for improving patient outcomes and reducing disease burden. Further analysis of the molecular mechanisms underlying hereditary cardiomyopathies is critical for identifying novel therapeutic targets and advancing precision medicine approaches. By comprehensively exploring the genetic underpinnings of cardiomyopathies, this paper contributes to our understanding of these complex diseases and highlights the potential for innovative interventions to enhance patient care in the field of cardiovascular medicine.

Global consensus statement on simulation-based practice in healthcare

Simulation plays a pivotal role in addressing universal healthcare challenges, reducing education inequities, and improving mortality, morbidity and patient experiences. It enhances healthcare processes and systems, contributing significantly to the development of a safety culture within organizations. It has proven to be cost-effective and successful in enhancing team performance, fostering workforce resilience and improving patient outcomes.Through an international collaborative effort, an iterative consultation process was conducted with 50 societies operating across 67 countries within six continents. This process revealed common healthcare challenges and simulation practices worldwide. The intended audience for this statement includes policymakers, healthcare organization leaders, health education institutions, and simulation practitioners. It aims to establish a consensus on the key priorities for the broad adoption of exemplary simulation practice that benefits patients and healthcare workforces globally.Key recommendations Advocating for the benefits that simulation provides to patients, staff and organizations is crucial, as well as promoting its adoption and integration into daily learning and practice throughout the healthcare spectrum. Low-cost, high-impact simulation methods should be leveraged to expand global accessibility and integrate into system improvement processes as well as undergraduate and postgraduate curricula. Support at institutional and governmental level is essential, necessitating a unified and concerted approach in terms of political, strategic and financial commitment.It is imperative that simulation is used appropriately, employing evidence-based quality assurance approaches that adhere to recognized standards of best practice. These standards include faculty development, evaluation, accrediting, credentialing, and certification.We must endeavor to provide equitable and sustainable access to high-quality, contextually relevant simulation-based learning opportunities, firmly upholding the principles of equity, diversity and inclusion. This should be complemented with a renewed emphasis on research and scholarship in this field.Call for action We urge policymakers and leaders to formally acknowledge and embrace the benefits of simulation in healthcare practice and education. This includes a commitment to sustained support and a mandate for the application of simulation within education, training, and clinical environments.We advocate for healthcare systems and education institutions to commit themselves to the goal of high-quality healthcare and improved patient outcomes. This commitment should encompass the promotion and resource support of simulation-based learning opportunities for individuals and interprofessional teams throughout all stages and levels of a caregiver’s career, in alignment with best practice standards.We call upon simulation practitioners to champion healthcare simulation as an indispensable learning tool, adhere to best practice standards, maintain a commitment to lifelong learning, and persist in their fervent advocacy for patient safety.This statement, the result of an international collaborative effort, aims to establish a consensus on the key priorities for the broad adoption of exemplary simulation practice that benefits patients and healthcare workforces globally.

Pilot Validation of a 3-Dimensional Printed Pituitary Adenoma, Vascular Injury, and Cerebrospinal Fluid Leak Surgical Simulator.

Endoscopic skull base surgery is a subspecialty field which would benefit significantly from high-fidelity surgical simulators. Giving trainees the opportunity to flatten their learning curve by practicing a variety of procedures on surgical simulators will inevitably improve patient outcomes. Four neurosurgeons, 8 otolarynologists, and 6 expert course faculty agreed to participate. All participants were asked to perform a transsphenoidal exposure and resection of a pituitary adenoma, repair a cerebrospinal fluid (CSF) leak, control a carotid injury, and repair a skull base defect. The content, face, and construct validity of the 3-dimensional printed model was examined. The heart rate of the participants significantly increased from baseline when starting the carotid injury simulation (mean 90 vs 121, P = .029) and significantly decreased once the injury was controlled (mean 121 vs 110, P = .033, respectively). The participants reported a significant improvement in anxiety in facing a major vascular injury, as well as an increase in their confidence in management of major vascular injury, resecting a pituitary adenoma and repair of a CSF leak using a 5-point Likert scale (mean 4.42 vs 3.58 P = .05, 2 vs 3.25 P < .001, 2.36 vs 4.27 P < .001 and 2.45 vs 4.0 P = .001, respectively). The mean Objective Structured Assessment of Technical Skills score for experienced stations was 4.4, significantly higher than the Objective Structured Assessment of Technical Skills score for inexperienced stations (mean 3.65, P = .016). We have demonstrated for the first time a validated 3-dimensional printed surgical simulator for endoscopic pituitary surgery that allows surgeons to practice a transsphenoidal approach, surgical resection of a pituitary adenoma, repair of a CSF leak in the diaphragma sellae, control of a carotid injury, and repair of skull base defect.

Application of biological agents in the treatment of anti-neutrophil cytoplasmic antibody-associated vasculitis

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) has been traditionally treated using glucocorticoids and immunosuppressants. However, these treatment modes are associated with high recurrence AAV rates and adverse reactions. Therefore, treatment strategies for AAV need to be urgently optimized. The efficacy and safety of biological agents in the treatment of vasculitis have been clinically validated. This review comprehensively summarizes the evidence-based support for the clinical use of existing biological agents in AAV. The findings reveal that multiple biological agents not only effectively reduce the adverse reactions associated with glucocorticoids and immunosuppressants but also demonstrate significant therapeutic efficacy. Notably, rituximab, an anti-CD20 antibody, has emerged as a first-line treatment option for AAV. Mepolizumab has shown promising results in relapsed and refractory eosinophilic granulomatosis with polyangiitis. Other biological agents targeting cytokines, complement, and other pathways have also demonstrated clinical benefits in recent studies. The widespread application of biological agents provides new insights into the treatment of AAV and is expected to drive further clinical research. These advancements not only improve patient outcomes but also offer more possibilities and hope in the field of AAV treatment.

Demographic and Clinical Characteristics of Females with Breast Cancer in Iraq with Respect to Age: A Single-Center Study

Abstract Background Breast cancer is a growing concern in Iraq, posing significant health challenges for Iraqi women. The objective of this study was to describe the demographic and clinical characteristics associated with breast cancer among Iraqi women, with a specific focus on age-related differences. Materials and Methods Medical records of 500 female patients diagnosed with breast cancer between 2017 and 2022 were retrospectively analyzed. Demographic information, clinical history, presentation details, treatment modalities, and outcomes were collected and analyzed. Results Among the studied cohort, 62.4% of breast cancer cases were diagnosed in women aged 50 and older. Marital status correlated significantly with age, with 89.9% of those diagnosed before 50 being married compared to 74.7% of those diagnosed after 50. Notably, 48% of patients diagnosed before 50 had breastfeeding duration of 1–3 months, contrasting with 28.5% of those diagnosed after 50. Diabetes mellitus, hypertension, hypercholesterolemia, heart disease, and vitamin D deficiency were significantly more prevalent among patients diagnosed after 50. Clinical presentation revealed 67.8% with tumor stage II. Metastasis to lymph nodes occurred in approximately 50% of patients. Among patients who were diagnosed after 50 years of age, 34% had metastasis, compared to 22.9% of those diagnosed before the age of 50. Mortality rates were higher in patients diagnosed after 50 (23%) compared to those under 50 (12.8%). Conclusion The findings emphasize the need for targeted public health campaigns, early detection initiatives, and treatment optimization to improve patient outcomes and mitigate the burden of breast cancer.

Predictive modelling and identification of key risk factors for stroke using machine learning

Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. However, addressing hidden risk factors and achieving accurate prediction become particularly challenging in the presence of imbalanced and missing data. This study encompasses three imputation techniques to deal with missing data. To tackle data imbalance, it employs the synthetic minority oversampling technique (SMOTE). The study initiates with a baseline model and subsequently employs an extensive range of advanced models. This study thoroughly evaluates the performance of these models by employing k-fold cross-validation on various imbalanced and balanced datasets. The findings reveal that age, body mass index (BMI), average glucose level, heart disease, hypertension, and marital status are the most influential features in predicting strokes. Furthermore, a Dense Stacking Ensemble (DSE) model is built upon previous advanced models after fine-tuning, with the best-performing model as a meta-classifier. The DSE model demonstrated over 96% accuracy across diverse datasets, with an AUC score of 83.94% on imbalanced imputed dataset and 98.92% on balanced one. This research underscores the remarkable performance of the DSE model, compared to the previous research on the same dataset. It highlights the model's potential for early stroke detection to improve patient outcomes.

A novel hybrid model for lung and colon cancer detection using pre-trained deep learning and KELM

Cancer poses a significant threat to life due to its aggressive nature, high potential for metastasis, and heterogeneity. Globally, both the men and women are mostly affected by lung and colon (LC) cancer. Early and accurate detection is essential to enhance the well-being of patients facing LC cancer. The traditional techniques face challenges such as low accuracy and high execution time. This work combines the pre-trained deep learning (DL) models like ResNet-50, InceptionV3, and DenseNet with Kernel Extreme Learning Machine (KELM) for the accurate and fast diagnosis of LC cancer using histopathology images. The pre-trained DL models provide a strong foundation for feature extraction, capturing intricate patterns indicative of cancerous tissues. Feature fusion combines the complementary strengths of multiple pre-trained models, enhancing the method's ability to capture diverse features and improve classification accuracy. KELM efficiently handles the high-dimensional feature space generated by the DL models, ensuring fast and accurate classification. Mutation Boosted Dwarf Mongoose Optimization Algorithm (MB-DMOA) further optimizes the model parameters, mitigating the risk of getting stuck in local optima and facilitating better convergence towards the global optimum. By leveraging the advantages of each technique and integrating them into a cohesive framework, the proposed approach achieves superior performance in diagnosing LC cancer, ultimately improving patient outcomes. The proposed study attains accuracy (98.9%), F1-score (97.6%), specificity (96.5%), precision (96.7%) and sensitivity (95.8%). The results illuminate the efficacy of this approach within therapeutic environments.