Exploring Predictive Factors for Heart Failure Progression in Hypertensive Patients Based on Medical Diagnosis Data from the MIMIC-IV Database.

Heart failure is associated with a significant mortality rate, and an elevated prevalence of this condition has been noted among hypertensive patients. The identification of predictive factors for heart failure progression in hypertensive individuals is crucial for early intervention and improved patient outcomes. In this study, we aimed to identify these predictive factors by utilizing medical diagnosis records for hypertension patients from the MIMIC-IV database. In particular, we employed only diagnostic history prior to hypertension to enable patients to anticipate the onset of heart failure at the moment of hypertension diagnosis. In the methodology, chi-square tests and XGBoost modeling were applied to examine age-specific predictive factors across four groups: AL (all ages), G1 (0 to 65 years), G2 (65 to 80 years), and G3 (over 80 years). As a result, the chi-square tests identified 34, 28, 20, and 10 predictive factors for the AL, G1, G2, and G3 groups, respectively. Meanwhile, the XGBoost modeling uncovered 19, 21, 27, and 33 predictive factors for these respective groups. Ultimately, our findings reveal 21 overall predictive factors, encompassing conditions such as atrial fibrillation, the use of anticoagulants, kidney failure, obstructive pulmonary disease, and anemia. These factors were assessed through a comprehensive review of the existing literature. We anticipate that the results will offer valuable insights for the risk assessment of heart failure in hypertensive patients.

UbNiRF: A Hybrid Framework Based on Null Importances and Random Forest that Combines Multiple Features to Predict Ubiquitination Sites in Arabidopsis thaliana and Homo sapiens.

BACKGROUND: Ubiquitination is a crucial post-translational modification of proteins that regulates diverse cellular functions. Accurate identification of ubiquitination sites in proteins is vital for understanding fundamental biological mechanisms, such as cell cycle and DNA repair. Conventional experimental approaches are resource-intensive, whereas machine learning offers a cost-effective means of accurately identifying ubiquitination sites. The prediction of ubiquitination sites is species-specific, with many existing models being tailored for Arabidopsis thaliana (A. thaliana) and Homo sapiens (H. sapiens). However, these models have shortcomings in sequence window selection and feature extraction, leading to suboptimal performance. METHODS: This study initially employed the chi-square test to determine the optimal sequence window. Subsequently, a combination of six features was assessed: Binary Encoding (BE), Composition of K-Spaced Amino Acid Pair (CKSAAP), Enhanced Amino Acid Composition (EAAC), Position Weight Matrix (PWM), 531 Properties of Amino Acids (AA531), and Position-Specific Scoring Matrix (PSSM). Comparative evaluation involved three feature selection methods: Minimum Redundancy-Maximum Relevance (mRMR), Elastic net, and Null importances. Alongside these were four classifiers: Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The Null importances combined with the RF model exhibited superior predictive performance, and was denoted as UbNiRF (A. thaliana: ArUbNiRF; H. sapiens: HoUbNiRF). RESULTS: A comprehensive assessment indicated that UbNiRF is superior to existing prediction tools across five performance metrics. It notably excelled in the Matthews Correlation Coefficient (MCC), with values of 0.827 for the A. thaliana dataset and 0.781 for the H. sapiens dataset. Feature analysis underscores the significance of integrating six features and demonstrates their critical role in enhancing model performance. CONCLUSIONS: UbNiRF is a valuable predictive tool for identifying ubiquitination sites in both A. thaliana and H. sapiens. Its robust performance and species-specific discovery capabilities make it extremely useful for elucidating biological processes and disease mechanisms associated with ubiquitination.

Evaluating the Diagnostic Performance of Large Language Models on Complex Multimodal Medical Cases.

Large language models showed interpretative reasoning in solving diagnostically challenging medical cases.

Hospital Readmission Rates for Patients Receiving In-Person vs. Telemedicine Discharge Follow-Up Care.

INTRODUCTION: Unplanned readmissions can be avoided by standardizing and improving the coordination of care after discharge. Telemedicine has been increasingly utilized; however, the quality of this care has not been well studied. Standardized measures can provide an objective comparison of care quality. The purpose of our study was to compare quality performance transitions of care management in the office vs telemedicine. METHODS: The Epic SlicerDicer tool was used to compare the percentage of encounters that were completed via telemedicine (video visits); or via in-person for comparison, Chi-squared tests were used. RESULTS: A total of 13,891 patients met the inclusion criteria during the study time frame. There were 12,846 patients in the office and 1,048 in the telemedicine cohort. The office readmission rate was 11.9% with 1,533 patients out of 12,846 compared with telemedicine with the rate of readmission at 12.1% with 126 patients out of 1,045 patients. The P-value for the Chi-squared test between the prepandemic and study time frame was 0.15 and 0.95, respectively. Demographic comparability was seen. DISCUSSION: Our study found a comparable readmission rate between patients seen via in-office and telemedicine for Transitions of Care Management (TCM) encounters. The findings of this study support the growing body of evidence that telemedicine augments quality performance while reducing cost and improving access without negatively impacting HEDIS performance in health care systems. CONCLUSION: Telemedicine poses little threat of negatively impacting HEDIS performance and might be as effective as posthospitalization traditional office care transitions of care management.

Machine Learning to Identify Clusters in Family Medicine Diplomate Motivations and Their Relationship to Continuing Certification Exam Outcomes: Findings and Potential Future Implications.

BACKGROUND: The potential for machine learning (ML) to enhance the efficiency of medical specialty boards has not been explored. We applied unsupervised ML to identify archetypes among American Board of Family Medicine (ABFM) Diplomates regarding their practice characteristics and motivations for participating in continuing certification, then examined associations between motivation patterns and key recertification outcomes. METHODS: Diplomates responding to the 2017 to 2021 ABFM Family Medicine continuing certification examination surveys selected motivations for choosing to continue certification. We used Chi-squared tests to examine difference proportions of Diplomates failing their first recertification examination attempt who endorsed different motivations for maintaining certification. Unsupervised ML techniques were applied to generate clusters of physicians with similar practice characteristics and motivations for recertifying. Controlling for physician demographic variables, we used logistic regression to examine the effect of motivation clusters on recertification examination success and validated the ML clusters by comparison with a previously created classification schema developed by experts. RESULTS: ML clusters largely recapitulated the intrinsic/extrinsic framework devised by experts previously. However, the identified clusters achieved a more equal partitioning of Diplomates into homogenous groups. In both ML and human clusters, physicians with mainly extrinsic or mixed motivations had lower rates of examination failure than those who were intrinsically motivated. DISCUSSION: This study demonstrates the feasibility of using ML to supplement and enhance human interpretation of board certification data. We discuss implications of this demonstration study for the interaction between specialty boards and physician Diplomates.

Evaluating fit indices in a multilevel latent growth model with unbalanced design: a Monte Carlo study.

This study informed researchers about the performance of different level-specific and target-specific model fit indices in the Multilevel Latent Growth Model (MLGM) with unbalanced design. As the use of MLGMs is relatively new in applied research domain, this study helped researchers using specific model fit indices to evaluate MLGMs. Our simulation design factors included three levels of number of groups (50, 100, and 200) and three levels of unbalanced group sizes (5/15, 10/20, and 25/75), based on simulated datasets derived from a correctly specified MLGM. We evaluated the descriptive information of the model fit indices under various simulation conditions. We also conducted ANOVA to calculated the extent to which these fit indices could be influenced by different design factors. Based on the results, we made recommendations for practical and theoretical research about the fit indices. CFI- and TFI-related fit indices performed well in the MLGM and could be trustworthy to use to evaluate model fit under similar conditions found in applied settings. However, RMSEA-related fit indices, SRMR-related fit indices, and chi square-related fit indices varied by the factors included in this study and should be used with caution for evaluating model fit in the MLGM.

Examining the performance of the chi-square difference test when the unrestricted model is slightly misspecified.

Structural equation models are used to model the relationships between latent constructs and observable behaviors such as survey responses. Researchers are often interested in testing nested models to determine whether additional constraints that create a more parsimonious model are also supported by the data. A popular statistical tool for nested model comparison is the chi-square difference test. However, there is some evidence that this test performs suboptimally when the unrestricted model is misspecified. In this paper, we examine the type I error rate of the difference test within the context of single-group confirmatory factor analyses when the less restricted model is misspecified but the constraints imposed by the restricted model are correct. Using empirical simulations and analytic approximations, we find that the chi-square difference test is robust to many but not all forms of realistically sized misspecification in the unrestricted model.

Exploring post-COVID-19 health effects and features with advanced machine learning techniques.

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and based on these factors survey data were collected from COVID-recovered patients in Bangladesh. Employing diverse machine learning algorithms, we utilised the best prediction model for post-COVID-19 factors. Initial findings from statistical analysis were further validated using Chi-square to demonstrate significant relationships among these elements. Additionally, Pearson's coefficient was utilized to indicate positive or negative associations among various physiological and neurological factors in the post-COVID-19 state. Finally, we determined the most effective machine learning model and identified key features using analytical methods such as the Gini Index, Feature Coefficients, Information Gain, and SHAP Value Assessment. And found that the Decision Tree model excelled in identifying crucial features while predicting the extent of post-COVID-19 impact.

Diabetes and the social, biologic, and behavioral determinants of endometrial cancer in the United States.

BACKGROUND: Endometrial cancer is one of the most common types of cancer that affects women's reproductive system. The risk of endometrial cancer is associated with biologic, behavioral and social determinants of health (SDOH). The focus of the work is to investigate the cumulative effect of this cluster of covariates on the odds of endometrial cancer that heretofore have only been considered individually. METHODS: We conducted a quantitative study using the Behavioral Risk Factor Surveillance System (BRFSS) national data collected in 2020. Data analysis using weighted Chi-square test and weighted logistic regression were carried out on 84,118 female study participants from the United States. RESULTS: Women with diabetes mellitus were approximately twice as likely to have endometrial cancer compared to women without diabetes (OR 1.54; 95%CI: 1.01-2.34). Biologic factors that included obesity (OR 3.10; 95% CI: 1.96-4.90) and older age (with ORs ranging from 2.75 to 7.21) had a significant increase in the odds of endometrial cancer compared to women of normal weight and younger age group of 18 to 44. Among the SDOH, attending college (OR 1.83; 95% CI: 1.12-3.00) was associated with increased odds of endometrial cancer, while renting a home (OR 0.50; 95% CI: 0.28-0.88), having other arrangements (OR 0.05; 95% CI: 0.02-0.16), being divorced (OR 0.55; 95% CI: 0.30-0.99), and having higher incomes ranging from $35,000 to $50,000 (OR 0.35; 95% CI: 0.16-0.78), and above $50,000 (OR 0.29; 95% CI: 0.14-0.62), were all associated with decreased odds of endometrial cancer. As for race, Black women (OR 0.24; 95% CI: 0.07-0.84) and women of other races (OR 0.37; 95% CI: 0.15-0.88) were shown to have lower odds of endometrial cancer compared to White women. CONCLUSION: Our results revealed the importance of adopting a comprehensive approach to the study of the associated factors of endometrial cancer by including social, biologic, and behavioral determinants of health. The observed social inequity in endometrial cancer among women needs to be addressed through effective policies and changes in social structures to advocate for a standardized healthcare system that ensures equitable access to preventive measures and quality of care.

Oral Cannabidiol for Seborrheic Dermatitis in Patients With Parkinson Disease: Randomized Clinical Trial.

BACKGROUND: Seborrheic dermatitis (SD) affects 18.6%-59% of persons with Parkinson disease (PD), and recent studies provide evidence that oral cannabidiol (CBD) therapy could reduce sebum production in addition to improving motor and psychiatric symptoms in PD. Therefore, oral CBD could be useful for improving symptoms of both commonly co-occurring conditions. OBJECTIVE: This study investigates whether oral CBD therapy is associated with a decrease in SD severity in PD. METHODS: Facial photographs were collected as a component of a randomized (1:1 CBD vs placebo), parallel, double-blind, placebo-controlled trial assessing the efficacy of a short-term 2.5 mg per kg per day oral sesame solution CBD-rich cannabis extract (formulated to 100 mg/mL CBD and 3.3 mg/mL THC) for reducing motor symptoms in PD. Participants took 1.25 mg per kg per day each morning for 4 ±1 days and then twice daily for 10 ±4 days. Reviewers analyzed the photographs independently and provided a severity ranking based on the Seborrheic Dermatitis Area and Severity Index (SEDASI) scale. Baseline demographic and disease characteristics, as well as posttreatment SEDASI averages and the presence of SD, were analyzed with 2-tailed t tests and Pearson χ2 tests. SEDASI was analyzed with longitudinal regression, and SD was analyzed with generalized estimating equations. RESULTS: A total of 27 participants received a placebo and 26 received CBD for 16 days. SD severity was low in both groups at baseline, and there was no treatment effect. The risk ratio for patients receiving CBD, post versus pre, was 0.69 (95% CI 0.41-1.18; P=.15), compared to 1.20 (95% CI 0.88-1.65; P=.26) for the patients receiving the placebo. The within-group pre-post change was not statistically significant for either group, but they differed from each other (P=.07) because there was an estimated improvement for the CBD group and an estimated worsening for the placebo group. CONCLUSIONS: This study does not provide solid evidence that oral CBD therapy reduces the presence of SD among patients with PD. While this study was sufficiently powered to detect the primary outcome (efficacy of CBD on PD motor symptoms), it was underpowered for the secondary outcomes of detecting changes in the presence and severity of SD. Multiple mechanisms exist through which CBD can exert beneficial effects on SD pathogenesis. Larger studies, including participants with increased disease severity and longer treatment periods, may better elucidate treatment effects and are needed to determine CBD's true efficacy for affecting SD severity. TRIAL REGISTRATION: ClinicalTrials.gov NCT03582137; https://clinicaltrials.gov/ct2/show/NCT03582137.

Power analyses for measurement model misspecification and response shift detection with structural equation modeling.

PURPOSE: Statistical power for response shift detection with structural equation modeling (SEM) is currently underreported. The present paper addresses this issue by providing worked-out examples and syntaxes of power calculations relevant for the statistical tests associated with the SEM approach for response shift detection. METHODS: Power calculations and related sample-size requirements are illustrated for two modelling goals: (1) to detect misspecification in the measurement model, and (2) to detect response shift. Power analyses for hypotheses regarding (exact) overall model fit and the presence of response shift are demonstrated in a step-by-step manner. The freely available and user-friendly R-package lavaan and shiny-app 'power4SEM' are used for the calculations. RESULTS: Using the SF-36 as an example, we illustrate the specification of null-hypothesis (H0) and alternative hypothesis (H1) models to calculate chi-square based power for the test on overall model fit, the omnibus test on response shift, and the specific test on response shift. For example, we show that a sample size of 506 is needed to reject an incorrectly specified measurement model, when the actual model has two-medium sized cross loadings. We also illustrate power calculation based on the RMSEA index for approximate fit, where H0 and H1 are defined in terms of RMSEA-values. CONCLUSION: By providing accessible resources to perform power analyses and emphasizing the different power analyses associated with different modeling goals, we hope to facilitate the uptake of power analyses for response shift detection with SEM and thereby enhance the stringency of response shift research.

Clinical evaluation and treatment outcomes of caustic ingestion injuries in Syrian pediatric patients: A retrospective study: Short title: High-dose steroids for pediatric caustic ingestions in Syria.

Introduction: Accidental ingestion of caustic agents poses a significant concern in pediatric emergency departments globally. It is a growing public health concern in low-to-middle income countries, which often lack comprehensive data reporting. This study examines high doses of corticosteroid treatment outcomes of caustic ingestion injuries in Syrian pediatric patients, addressing clinical features, and associated variables. Methods and materials: A retrospective observational study was conducted at University Pediatric Hospital from January 2016 to January 2019. Medical records were reviewed for patients aged <10 years with esophagoscopy-confirmed grade IIa, IIb, or III burns. Data collected included sociodemographics, esophagoscopy results, treatment details, and outcomes. Results: Among 114 pediatric patients, 76 (67%) were males and 38 (33%) were females. Age groups included <1 year (11%), 1-3 years (39%), 3-5 years (29%), 5-7 years (11%), and >7 years (11%). Alkaline burns accounted for 54% of injuries, acidic for 32%, and other substances for 13%. Complications included bleeding (19%) and psychomotor disability (7%). The most common burn site was the entire esophagus (62%), with 81% having grade II burns. Healing was achieved in 71% of patients with high doses of corticosteroids treatment, and 29% required dilation, with final 92% healing rate. Conclusion: The use of corticosteroids for esophageal strictures remains inconclusive, demanding further robust research with larger sample sizes and control groups. While our study revealed that high doses of corticosteroids treatment followed by esophageal dilation had a 92% success rate. However, our study demonstrates promising results, methodological limitations and absence of a control group underscore the need for more definitive evidence. Both alkali and acidic ingestion contribute to stricture development.

Cooling Performance Prediction of Particle-Based Radiative Cooling Film Considering Particle Size Distribution.

Here, we present a novel protocol concept for quantifying the cooling performance of particle-based radiative cooling (PBRC). PBRC, known for its high flexibility and scalability, emerges as a promising method for practical applications. The cooling power, one of the cooling performance indexes, is the typical quantitative performance index, representing its cooling capability at the surface. One of the primary obstacles to predicting cooling power is the difficulty of simulating the non-uniform size and shape of micro-nanoparticles in the PBRC film. The present work aims to develop an accurate protocol for predicting the cooling power of PBRC film using image processing and regression analysis techniques. Specifically, the protocol considers the particle size distribution through circle object detection on SEM images and determines the probability density function based on a chi-square test. To validate the proposed protocol, a PBRC structure with PDMS/Al2O3 micro-nanoparticles is fabricated, and the proposed protocol precisely predicts the measured cooling power with a 7.8% error. Through this validation, the proposed protocol proves its potential and reliability for the design of PBRC.

Studying Plant ER-PM Contact Site Localized Proteins Using Microscopy.

As in most eukaryotic cells, the plant endoplasmic reticulum (ER) network is physically linked to the plasma membrane (PM), forming ER-PM contact sites (EPCS). The protein complex required for maintaining the EPCS is composed of ER integral membrane proteins (e.g., VAP27, synaptotagmins), PM-associated proteins (e.g., NET3C), and the cytoskeleton. Here, we describe methods for studying EPCS structures and identifying possible EPCS-associated proteins. These include using artificially constructed reporters, GFP tagged protein expression followed by image analysis, and immunogold labelling at the ultrastructural level. In combination, these methods can be used to identify the location of putative EPCS proteins, which can aid in predicting their potential subcellular function.

Statin Use and Hepatocellular Carcinoma Risk: A Comprehensive Meta- Analysis and Systematic Review.

BACKGROUND: Hepatocellular Carcinoma (HCC) is a public health problem around the world. Several studies have investigated the association between statin use and the risk of HCC, however, more studies are needed in this field. OBJECTIVES: This systematic review and meta-analysis aimed to investigate the relationship between statin use and HCC risk. METHODS: Systematic searches of Web of Science, Scopus, PubMed, Cochrane, Science Direct, and Embase were conducted for studies published between 1980 and September 2023. Metaanalyses were performed using Stata 15 with a significance level of 0.05. RESULTS: The search retrieved 8,125 articles, of which 40 were included in the meta-analysis after applying eligibility criteria. The total sample was 5,732,948 participants, including 68,698 HCC cases. Statin use was associated with a 44% lower risk of HCC compared to non-use (RR 0.56, 95% CI 0.50-0.63, p < 0.001). The RR was 0.54 (0.42-0.69) in American countries, 0.52 (0.44-0.62) in Asian countries, and 0.63 (0.48-0.84) in European countries. The RR was 0.50 (0.42-0.60) in studies with a mean age <50 years and 0.61 (0.53-0.70) in studies with a mean age ≥50 years. No evidence of publication bias was found (Begg's test p = 0.718). CONCLUSION: This meta-analysis found statin use is associated with a significantly lower HCC risk. Statins may be a promising preventive intervention against HCC.

Permutation Tests for Assessing Potential Non-Linear Associations between Treatment Use and Multivariate Clinical Outcomes.

In many psychometric applications, the relationship between the mean of an outcome and a quantitative covariate is too complex to be described by simple parametric functions; instead, flexible nonlinear relationships can be incorporated using penalized splines. Penalized splines can be conveniently represented as a linear mixed effects model (LMM), where the coefficients of the spline basis functions are random effects. The LMM representation of penalized splines makes the extension to multivariate outcomes relatively straightforward. In the LMM, no effect of the quantitative covariate on the outcome corresponds to the null hypothesis that a fixed effect and a variance component are both zero. Under the null, the usual asymptotic chi-square distribution of the likelihood ratio test for the variance component does not hold. Therefore, we propose three permutation tests for the likelihood ratio test statistic: one based on permuting the quantitative covariate, the other two based on permuting residuals. We compare via simulation the Type I error rate and power of the three permutation tests obtained from joint models for multiple outcomes, as well as a commonly used parametric test. The tests are illustrated using data from a stimulant use disorder psychosocial clinical trial.

Enhancing prediction of tooth caries using significant features and multi-model classifier.

Background: Tooth decay, also known as dental caries, is a common oral health problem that requires early diagnosis and treatment to prevent further complications. It is a chronic disease that causes the gradual breakdown of the tooth's hard tissues, primarily due to the interaction of bacteria and dietary sugars. Results: While numerous investigations have focused on addressing this issue using image-based datasets, the outcomes have revealed limitations in their effectiveness. In a novel approach, this study focuses on feature-based datasets, coupled with the strategic integration of Principle Component Analysis (PCA) and Chi-square (chi2) for robust feature engineering. In the proposed model, features are generated using PCA, utilizing a voting classifier ensemble consisting of Extreme Gradient Boosting (XGB), Random Forest (RF), and Extra Trees Classifier (ETC) algorithms. Discussion: Extensive experiments were conducted to compare the proposed approach with the chi2 features and machine learning models to evaluate its efficacy for tooth caries detection. The results showed that the proposed voting classifier using PCA features outperformed the other approaches, achieving an accuracy, precision, recall, and F1 score of 97.36%, 96.14%, 96.84%, and 96.65%, respectively. Conclusion: The study demonstrates that the utilization of feature-based datasets and PCA-based feature engineering, along with a voting classifier ensemble, significantly improves tooth caries detection accuracy compared to image-based approaches. The achieved high accuracy, precision, recall, and F1 score emphasize the potential of the proposed model for effective dental caries detection. This study provides new insights into the potential of innovative methodologies to improve dental healthcare by evaluating their effectiveness in addressing prevalent oral health issues.

Association between risk factors and migraine in Pakistani females.

BACKGROUND: Migraine is a typical cripple issue of the brain identified with cerebral pain which is an indication of numerous health conditions. About 18% of women (27 million) and 6% of men (10 million) are afflicted by migraine in the United States. Based on a case-control study, to explore the different risk factors, causing migraine in females and examine the association between risk factors and migraine. METHODS: A sample of 1055 individuals were selected in different areas of Lahore from September 2019 to March 2020. The information was obtained by using the direct interview method and questionnaire method. Descriptive analysis, bivariate analysis and binary logistic regression analysis were carried out in data analysis. RESULTS: Among 1055 individuals 740 cases and 315 controls were included. In a binary logistic regression model, physical activities, stress, summer season, menstruation and morning were the risk factors that cause migraine and these were found to be positively significant with the odds ratios and 95% confidence interval of odds ratios (1.399; 1.122-1.746), (1.510; 1.187-1.922), (1.595; 1.374-1.851), (1.513; 1.247-1.836) and (1.309; 1.028-1.665) respectively. Nausea, isolation and back head pain were caused by migraine and these were found positively significant with the odds ratios and 95% confidence interval of odds ratios(1.290; 1.122-1.484), (1.882; 1.617-2.190) and (1.285; 1.123-1.471) respectively. CONCLUSIONS: Stress, physical Activities and Menstruation increase the risk of migraine but weight loss, Breakfast, lunch, thirst, injury and Second trimester during pregnancy reduce the risk of migraine.

Optimizing fetal health prediction: Ensemble modeling with fusion of feature selection and extraction techniques for cardiotocography data.

Cardiotocography (CTG) captured the fetal heart rate and the timing of uterine contractions. Throughout pregnancy, CTG intelligent categorization is crucial for monitoring fetal health and preserving proper fetal growth and development. Since CTG provides information on the fetal heartbeat and uterus contractions, which helps determine if the fetus is pathologic or not, obstetricians frequently use it to evaluate a child's physical health during pregnancy. In the past, obstetricians have artificially analyzed CTG data, which is time-consuming and inaccurate. So, developing a fetal health categorization model is crucial as it may help to speed up the diagnosis and treatment and conserve medical resources. The CTG dataset is used in this study. To diagnose the illness, 7 machine learning models are employed, as well as ensemble strategies including voting and stacking classifiers. In order to choose and extract the most significant and critical attributes from the dataset, Feature Selection (FS) techniques like ANOVA and Chi-square, as well as Feature Extraction (FE) strategies like Principal Component Analysis (PCA) and Independent Component Analysis (ICA), are being used. We used the Synthetic Minority Oversampling Technique (SMOTE) approach to balance the dataset because it is unbalanced. In order to forecast the illness, the top 5 models are selected, and these 5 models are used in ensemble methods such as voting and stacking classifiers. The utilization of Stacking Classifiers (SC), which involve Adaboost and Random Forest (RF) as meta-classifiers for disease detection. The performance of the proposed SC with meta-classifier as RF model, which incorporates Chi-square with PCA, outperformed all other state-of-the-art models, achieving scores of 98.79%,98.88%,98.69%,96.32%, and 98.77% for accuracy, precision, recall, specificity, and f1-score respectively.

Association of Facial Wrinkles With Different Facial Forms in the Population of Maharashtra: A Prospective Observational Study.

INTRODUCTION: Wrinkles commonly manifest in various areas of the face as individuals age. This study aimed to assess the association between facial wrinkles and different facial forms. MATERIALS AND METHODS: An observational, prospective study was conducted on the facial photographs of 400 subjects aged 40-60 years, which were divided into four groups of 100 subjects each: Group 1, square facial form; Group 2, ovoid facial form; Group 3, square tapered facial form; and Group 4, tapered facial form. All groups had almost equal distributions of males and females. Six types of facial wrinkles were studied, namely, forehead, glabellar, canthal, nasolabial, wrinkles at the corner of the mouth, and perioral wrinkles. Analysis of variance (ANOVA) was used for intergroup comparison, and an independent Student's t-test was used to assess gender differences in facial wrinkles. RESULTS: Significant gender differences were observed for forehead wrinkles in Groups 1 and 3, canthal wrinkles in Groups 1 and 2, and right perioral wrinkles in Group 1 (p<0.05). There were non-significant gender differences between right and left-side facial wrinkles (p>0.05). Significant differences between the groups were observed for all facial wrinkles between the right and left sides of the face (p<0.05). There was a significant difference between the groups for the presence of glabellar, corner of the mouth, and perioral wrinkles, with the presence of higher wrinkles in ovoid and tapered facial forms compared to square and square tapered facial forms (p<0.05). CONCLUSIONS: Females had more facial wrinkles than males, predominantly in the forehead region. The least prominent wrinkles were observed in the perioral region of the face. Glabellar, corner of the mouth, and perioral wrinkles were predominantly observed in ovoid and tapered facial forms.