Predicting cervical cancer biopsy results using demographic and epidemiological parameters: a custom stacked ensemble machine learning approach

The human papillomavirus (HPV) is responsible for most cervical cancer cases worldwide. This gynecological carcinoma causes many deaths, even though it can be treated by removing malignant tissues at a preliminary stage. In many developing countries, patients do not undertake medical examinations due to the lack of awareness, hospital resources and high testing costs. Hence, it is vital to design a computer aided diagnostic method which can screen cervical cancer patients. In this research, we predict the probability risk of contracting this deadly disease using a custom stacked ensemble machine learning approach. The technique combines the results of several machine learning algorithms on multiple levels to produce reliable predictions. In the beginning, a deep exploratory analysis is conducted using univariate and multivariate statistics. Later, the one-way ANOVA, mutual information and Pearson’s correlation techniques are utilized for feature selection. Since the data was imbalanced, the Borderline-SMOTE technique was used to balance the data. The final stacked machine learning model obtained an accuracy, precision, recall, F1-score, area under curve (AUC) and average precision of 98%, 97%, 99%, 98%, 100% and 100%, respectively. To make the model explainable and interpretable to clinicians, explainable artificial intelligence algorithms such as Shapley additive values (SHAP), local interpretable model agnostic explanation (LIME), random forest and ELI5 have been effectively utilized. The optimistic results indicate the potential of automated frameworks to assist doctors and medical professionals in diagnosing and screening potential cervical cancer patients

No Mere Reflection: Mirrors as Windows on Russian Culture

This essay traces the development of mirror use in Russia from the medieval period to the modern day with particular attention to the dynamic interplay between the utilitarian and symbolic functions of this object. I examine how the discourse around mirrors in Russia was shaped by a preoccupation with border-crossing and identity that is distinctive to Russian culture as well as by mirror lore from other world traditions; and I demonstrate that the presence of mirrors shaped the production of imaginative literature in profound ways. The essay focuses on several key functions of the Russian mirror: as a site of self-creation and social interaction, as illusionistic décor, and as a tool for obtaining knowledge. In discussing human responses to mirror reflections, as documented in written texts, folklore, and film, my essay begins with personal mirrors in private spaces that conveyed the features of solitary beholders, and then moves outward to consider larger objects in public spaces, from street mirrors to glass skyscrapers, that were seen by multitudes and generated countless reflections in both the literal and the figurative sense

A Review of Ensemble Machine Learning Approach in Prediction of Diabetes Diseases

Data mining techniques improve efficiency and reliability in diabetes classification. Machine learning techniques are applied to predict medical dataset to safe human life. The large set of medical dataset is accessible in data warehousing which used in the real time application. Currently Diabetes Diseases (DD) is among the leading cause of death in the world. Data mining techniques are used to group and predict symptoms in medical dataset by different examiners. Data set from Pima Indian Diabetes Dataset (PIMA) were utilized to compare results with the results from other examiners. In this system, the most well known algorithms; K-Nearest Neighbor (KNN), Na�ve Bayes (NBs), Random Forest (RF) and J48 are used to construct an ensemble model. The experiment�s result reveals that an ensemble hybrid model increases the accuracy by combining individual techniques in to one. As a result, the model serves to be useful by doctors and Pathologist for the realistic health management of diabetes

Supervised Learning Models for the Preliminary Detection of COVID-19 in Patients Using Demographic and Epidemiological Parameters

The World Health Organization labelled the new COVID-19 breakout a public health crisis of worldwide concern on 30 January 2020, and it was named the new global pandemic in March 2020. It has had catastrophic consequences on the world economy and well-being of people and has put a tremendous strain on already-scarce healthcare systems globally, particularly in underdeveloped countries. Over 11 billion vaccine doses have already been administered worldwide, and the benefits of these vaccinations will take some time to appear. Today, the only practical approach to diagnosing COVID-19 is through the RT-PCR and RAT tests, which have sometimes been known to give unreliable results. Timely diagnosis and implementation of precautionary measures will likely improve the survival outcome and decrease the fatality rates. In this study, we propose an innovative way to predict COVID-19 with the help of alternative non-clinical methods such as supervised machine learning models to identify the patients at risk based on their characteristic parameters and underlying comorbidities. Medical records of patients from Mexico admitted between 23 January 2020 and 26 March 2022, were chosen for this purpose. Among several supervised machine learning approaches tested, the XGBoost model achieved the best results with an accuracy of 92%. It is an easy, non-invasive, inexpensive, instant and accurate way of forecasting those at risk of contracting the virus. However, it is pretty early to deduce that this method can be used as an alternative in the clinical diagnosis of coronavirus cases

A machine learning and explainable artificial intelligence approach for predicting the efficacy of hematopoietic stem cell transplant in pediatric patients

Cancer is a fatal disease that affects people of all ages, including children. It is one of the leading causes of death worldwide. According to World Health Organization, an estimated 400,000 children develop cancer yearly. Bone marrow transplantation (BMT) is a specialized treatment for patients suffering from certain types of cancer, such as myeloma, lymphoma, leukemia, and others. It usually includes extracting healthy cells from the donor’s bone marrow and replacing the existing ones in the patient’s body. However, the treatment can also cause complications such as graft-versus-host disease, organ damage, stem cell failure, new cancers, and infections. In this study, we use machine learning and explainable artificial intelligence (XAI) techniques to predict the survival rate of children undergoing Hematopoietic Stem Cell Transplants. Three feature selection techniques have been utilized for feature selection: Harris Hawks optimization, salp swarm optimization, and mutual information. The final custom stacked model delivered optimal results with accuracy, precision (89%), recall (88%), f1-score (88%), area under curve (AUC) (92%), and average precision (86%). In addition, XAI techniques such as Shapley additive values (SHAP), local interpretable model-agnostic explanations (LIME), ELI5, and QLattice have been used to make the models more precise, understandable, and interpretable. According to XAI, the most important features were relapse, donor age, recipient age, and platelet recovery time. The promising results point to the potential use of artificial intelligence in understanding the effectiveness of bone marrow transplants in children

Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence

Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. Stroke is a common cause of mortality among older people. Hence, loss of life and severe brain damage can be avoided if stroke is recognized and diagnosed early. Healthcare professionals can discover solutions more quickly and accurately using artificial intelligence (AI) and machine learning (ML). As a result, we have shown how to predict stroke in patients using heterogeneous classifiers and explainable artificial intelligence (XAI). The multistack of ML models surpassed all other classifiers, with accuracy, recall, and precision of 96%, 96%, and 96%, respectively. Explainable artificial intelligence is a collection of frameworks and tools that aid in understanding and interpreting predictions provided by machine learning algorithms. Five diverse XAI methods, such as Shapley Additive Values (SHAP), ELI5, QLattice, Local Interpretable Model-agnostic Explanations (LIME) and Anchor, have been used to decipher the model predictions. This research aims to enable healthcare professionals to provide patients with more personalized and efficient care, while also providing a screening architecture with automated tools that can be used to revolutionize stroke prevention and treatment

Bullying in the American Graduate Medical Education System: A National Cross-Sectional Survey.

OBJECTIVES:To deliver an estimate of bullying among residents and fellows in the United States graduate medical education system and to explore its prevalence within unique subgroups. DESIGN/SETTING/PARTICIPANTS:A national cross-sectional survey from a sample of residents and fellows who completed an online bullying survey conducted in June 2015. The survey was distributed using a chain sampling method that relied on electronic referrals from 4,055 training programs, with 1,791 residents and fellows completing the survey in its entirety. Survey respondents completed basic demographic and programmatic information plus four general bullying and 20 specific bullying behavior questions. Between-group differences were compared for demographic and programmatic stratifications. MAIN OUTCOMES/MEASURES:Self-reported subjected to workplace bullying from peers, attendings, nurses, ancillary staff, or patients in the past 12 months. RESULTS:Almost half of the respondents (48%) reported being subjected to bullying although both those subjected and not subjected reported experiencing ≥ 1 bullying behaviors (95% and 39% respectively). Attendings (29%) and nurses (27%) were the most frequently identified source of bullying, followed by patients, peers, consultants and staff. Attempts to belittle and undermine work and unjustified criticism and monitoring of work were the most frequently reported bullying behaviors (44% each), followed by destructive innuendo and sarcasm (37%) and attempts to humiliate (32%). Specific bullying behaviors were more frequently reported by female, non-white, shorter than < 5'8 and BMI ≥ 25 individuals. CONCLUSIONS/RELEVANCE:Many trainees report experiencing bullying in the United States graduate medical education programs. Including specific questions on bullying in the Accreditation Council for Graduate Medical Education annual resident/fellow survey, implementation of anti-bullying policies, and a multidisciplinary approach engaging all stakeholders may be of great value to eliminate these pervasive behaviors in the field of healthcare