Categorical Principal Component Analysis Research Articles

Injury, substance abuse, verbal abuse, and emotional and behavioral issues among adolescents in Uttarakhand, India: A categorical principal component analysis of global school-based health survey

Background: Adolescents in low-middle-income countries face increasing physical and mental health challenges. The present study aimed to assess status of injury, verbal abuse, substance use, and emotional behavior of adolescents of Uttarakhand, India, and evaluate the importance of each component. Methods: A cross-sectional study was conducted among adolescents in 13 government schools in Uttarakhand using the Global School-based Health Survey (GSHS). Categorical principal component analysis (CATPCA) using Varimax rotation was performed to analyze principal components among two domains of GSHS (substance abuse domain and verbal abuse and emotional behavior domain). Factorability was assessed using Kaiser–Meyer–Olkin and Bartlett’s test. Variance Accounted For index was used to evaluate the importance of each component. Results: A total of 634 adolescents completed the questionnaire. 41.3% students suffered from serious injury with a significant difference (P < 0.0001) between both genders. Girls were more likely to report having felt lonely most of the time or always in the past 12 months (11.0% vs 4.6%, P = 0.018). Boys were more likely to miss classes on 3 or more days (9.9% vs 4.9%, P = 0.043). Initiation of smoking cigarettes (9.6% vs 1.2%, P <0.0001) and tobacco chewing (3.4% vs 0.3%, P = 0.001) was more likely in 12–15-year-old boys. One-third of the students reported male guardians to use either alcohol or some form of tobacco. CATPCA yielded an 11-factor model accounting for 58.26% of variances with the most important principal component named “Tobacco (Smoking and Chewing): Initiation, Frequency and Attempt to Stop” (eigenvalue: 4.109). Conclusion: Significant differences in various items of injury, verbal abuse, substance use, and emotional behavior were recorded between boys and girls. CATPCA revealed patterns among injury, verbal abuse, emotional behavior, and substance abuse domains of GSHS by categorizing them into 11 components. On the basis of these patterns, prioritizing and development of appropriate school-based interventions may be implemented by various stakeholders of Uttarakhand.

Patient Health Engagement Model (PHE) in Affecting Patient Activation and Self-efficacy Among Cardiac Patients

Background: Heart patients must be able to actively manage and take care of their disease, medication, and symptoms. On the other hand, following self-management recommendations also requires patient activation. Objectives: This study assessed the relationships of patient health engagement model (PHE) with self-efficacy and activation in cardiac patients. Methods: This descriptive-analytical study is cross-sectional. The sample size was determined to be 207 cardiovascular patients. Subjects were randomly selected from a list of patients referred to a government hospital in the south of Iran. The data collection tools included the Patient activation measurement (PAM-19), health promotion strategies (SUPPH), and Patient Health Engagement Scale (PHE-S). Data were analyzed using descriptive tests, linear regression, optimal scaling, correlation test, and categorical principal components analysis (CATPCA). Results: All subjects were in the third stage of the PHE model (median = 3). A total of (49.5%) of subjects had good self-efficacy, and 39.7% of patients had a good activation score. The PHE model predicted self-efficacy (P < 0.001, R = 0.264) and patient activation (P < 0.001, R = 0.252). Patient Health Engagement had stronger predictive power for the active participation dimension (P < 0.001, R = 0.222). Conclusions: It seems that PHE is shaped according to the patients' level of activation and self-efficacy. However, more studies need to be conducted. By focusing on the emotional states, mental processes, and behavior of the patients, the PHE model can enhance their engagement in the chronic disease care process. Nonetheless, further research is necessary.

Development and psychometric testing of a scale to measure effective rural emergency transfer (RET)

BackgroundThe process of transferring patients from small rural primary care facilities to referral facilities impacts the quality of care and effectiveness of the referral healthcare system. The study aimed to develop and evaluate the psychometric properties of a scale measuring requirements for effective rural emergency transfer.MethodsAn exploratory sequential design was utilized to develop a scale designed to measure requirements for effective emergency transport. Phase one included a qualitative, interview study with 26 nursing transport providers. These transcripts were coded, and items developed for the proposed scale. Phase two included a content validity review by these 16 transport providers of the domains and items developed. Phase three included development and evaluation of psychometric properties of a scale designed to measure requirements for effective emergency transport. This scale was then tested initially with 84 items and later reduced to a final set of 58 items after completion by 302 transport nurses. The final scale demonstrated three factors (technology & tools; knowledge & skills; and organization). Each factor and the total score reported excellent scale reliability.ResultsThe initial item pool consisted of 84 items, generated, and synthesized from an extensive literature review and the qualitative descriptive study exploring nurses’ experiences in rural emergency patient transportation. A two-round modified Delphi method with experts generated a scale consisting of 58 items. A cross-sectional study design was used with 302 nurses in rural clinics and health in four rural health districts. A categorical principal components analysis identified three components explaining 63.35% of the total variance. The three factors, technology, tools, personal knowledge and skills, and organization, accounted for 27.32%, 18.15 and 17.88% of the total variance, respectively. The reliability of the three factors, as determined by the Categorical Principal Component Analysis (CATPCA)’s default calculation of the Cronbach Alpha, was 0.960, 0.946, and 0.956, respectively. The RET Cronbach alpha was 0.980.ConclusionsThe study offers a three-factor scale to measure the effectiveness of emergency patient transport in rural facilities to better understand and improve care during emergency patient transport.

Parallel multi-criteria decision analysis for sub-national prioritization of zoonoses and animal diseases in Africa: The case of Cameroon.

The use of multi-criteria decision analysis (MCDA) for disease prioritization at the sub-national level in sub-Sahara Africa (SSA) is rare. In this research, we contextualized MCDA for parallel prioritization of endemic zoonoses and animal diseases in The Adamawa and North regions of Cameroon. MCDA was associated to categorical principal component analysis (CATPCA), and two-step cluster analysis. Six and seven domains made of 17 and 19 criteria (out of 70) respectively were selected by CATPCA for the prioritization of zoonoses and animal diseases, respectively. The most influencing domains were "public health" for zoonoses and "control and prevention" for animal diseases. Twenty-seven zoonoses and 40 animal diseases were ranked and grouped in three clusters. Sensitivity analysis resulted in high correlation between complete models and reduced models showing the robustness of the simplification processes. The tool used in this study can be applied to prioritize endemic zoonoses and transboundary animal diseases in SSA at the sub-national level and upscaled at the national and regional levels. The relevance of MCDA is high because of its contextualization process and participatory nature enabling better operationalization of disease prioritization outcomes in the context of African countries or other low and middle-income countries.

Challenges in the most challenging course as perceived by the students of health sciences during the Covid‐19 pandemic: What are they and who were struggling the most?

AbstractThe immediate shift to remote teaching or distance learning, due to COVID‐19 management strategies, most notably limited in‐person contact, was abruptly implemented in universities worldwide. This process was demanding for both the instructors and the students, notwithstanding. The present study examined the challenges in a course attributed as the most challenging during the Covid‐19 pandemic by health sciences students of different socio‐demographic backgrounds, life circumstances, educational background and academic achievement (N = 743). A questionnaire was designed and translated to French employing the forward‐backward translation method. The factor structure and reliability were examined by Categorical Principal Component Analysis (CATPCA) and Cronbach's alpha, respectively. Chi‐square tests with post‐hoc examinations using adjusted standardized residuals and z‐tests of independent proportions were performed to investigate the group differences. Participants were bachelor and master students of Medicine, Pharmacy, Biomedicine, Physiotherapy, Public Health, Motor Sciences (Physical Education) and Dentistry from the University of Liege, Belgium. Results revealed that the most three reported challenges were difficult learning content, course intensity, feeling of stress and worry. Additionally, online learning implementation due to Covid‐19 measures, feeling of failure (not having learnt what was supposed to be learnt) and lack of instructors’ interaction and support were mentioned as the prominent challenges encountered. Furthermore, more bachelor, full‐time and female students, students who were in early stages of the learning trajectory and low and averaged achievers reported experiencing challenges with difficult learning content, course intensity, stress and online learning. The findings, thus, emphasized the role of faculty‐led and instructors’ support in the early stages of students’ learning trajectory and adequate attention to their well‐being if online learning is to be institutionalized.

The Evaluation of Relationships between Milk Composition Traits and Breeds with Categorical Principal Component Analysis in Akkaraman and Awasi Sheep

Background: This study aims to determine the relationship between milk composition traits and breed in the Akkaraman and Awasi sheep as well as to provide ease of interpretation by showing the relationships structure between variables and between categories of variables in two-dimensional space with Categorical principal component analysis. Methods: Categorical principal component analysis determines relationships between continuous and categorical variables as well as ordinal variables. It aims to reduce system dimensionality through optimal scaling while maintaining variable measurement levels (nominal, multiple nominal, ordinal and interval). In this research, data obtained from Akkaraman and Awasi Breed Sheep Raised by Public Hands in Tuşba District of Van Province were used. In order to determine relationship with breed, the traits were divided into two categories, “low” and “high” and all variables (9 variables) were considered together and a Categorical principal components analysis was performed. Result: As a results, Dimension 1 accounted for 35.58% of the total variation while dimension 2 accounted for 15.21%. Two dimensions together accounted for 50.79% of the variation. Thus it can be noted that Categorical principal component analysis can be used in the analysis of data sets containing a large number of different types of variables with linear or non-linear relationships between them.

Exploring the Botanical Origins of Bee-Collected Pollen: A Comprehensive Historical and Contemporary Analysis.

Bee-collected pollen is one of the most important bee products. In order to predict its chemical composition and nutritional value botanical origin of pollen plays a crucial role. This review intended to collect all available data published about botanical origin of pollen collected all around the world. Due to enourmous amount of data and variables nonlinear principal component analysis (NLPCA), by applying Categorical Principal Component Analysis (CATPCA), was conducted in order to try to determine any specifity and/or differences among samples. Also, importance of some plant families/genera/species for bees was monitored. Based on CATPCA results families can serve in order to distinct samples from North/South America. Also, some samples from Europe (Turkey and Serbia) were characterized with presence of some specific families. Genera were excellent tool to distinguish samples from different parts of Brazil as well as Australia, Asia and Africa. Due to high and specific biodiversity pollen samples obtained from Sonoran desert (USA) completely were separated during analysis. This review presents the first attempt to summarize and classify a large number of data about botanical sources of bee-collected pollen.

Preservative contact allergy in occupational dermatitis: a machine learning analysis.

Occupational dermatoses impose a significant socioeconomic burden. Allergic contact dermatitis related to occupation is prevalent among healthcare workers, cleaning service personnel, individuals in the beauty industry and industrial workers. Among risk factors, the exposure to preservatives is frequent, since they are extensively added in products for occupational use. The goal of this study is to investigate the contact allergy patterns in order to understand the linkage among hypersensitivity to preservatives, occupational profiles, patients' clinical and demographic characteristics. Patch test results were collected from monosensitized patients to Formaldehyde 2%, KATHON 0.02%, thimerosal 0.1%, and MDBGN 0.5%; information was also collected for an extended MOAHLFA (Male-Occupational-Atopic-Hand-Leg-Face-Age) index. To assess the relationship between allergen group and occupational-related ACD, the chi-square test for independence was utilized. To uncover underlying relationships in the data, multiple correspondence analysis (MCA) and categorical principal components analysis (CATPCA), which are machine learning approaches, were applied. Significant relationships were found between allergen group and: occupation class, atopy, hand, leg, facial, trunk, neck, head dermatitis, clinical characteristics, ICDRG 48h and ICDRG 72h clinical evaluation. MCA and CATPCA findings revealed a link among allergen group, occupation class, patients' demographic and clinical characteristics, the MOAHLFA index, and the ICDRG scores. Significant relationships were identified between the allergen group and various manifestations of dermatitis. The utilization of machine learning techniques facilitated the discernment of meaningful patterns in the data.

Measuring soundscape quality of urban environments using physiological indicators: Construction of physiological assessment dimensions and comparison with subjective dimensions

Residents and designers typically pursue healthy urban acoustic environments. The soundscape community has established internationally recommended standards for subjective questionnaires to evaluate soundscape quality; however, much work is still needed to standardise physiological assessments. This study measures the physiological responses and subjective evaluations of participants in 20 common sound sources (scenarios) in urban public open spaces, and discusses the potential dimensions when assessing the quality of urban soundscapes through physiological indicators. Categorical principal components analysis are employed to reduce the dimensionality of the physiological data. The results indicate that physiological Dimension 1 represents the Natural–Artificial dimension, and physiological Dimension 2 potentially represents the Regular–Irregular dimension, providing a rough estimate of regularity or predictability in soundscapes. Subjective data are used as a control for physiological data, subjective Dimension 1 can be summarised as the Calm (Pleasant)–Chaotic (Annoying) dimension, whereas subjective Dimension 2 can be summarised as Familiar (Congruent)–Unfamiliar (Incongruent). Through comparing and discussing the distributions in physiological and subjective models under different sound sources, it is found that both physiologically and subjectively, the foremost dimension (Dimension 1) represents comfort and pleasure, and natural sounds can be distinctly differentiated from noise through this dimension. Although its physiological significance is not clear, the two-dimensional physiological indicator model reveals distinctions between sound sources of various categories. These findings have implications for the planning and design of liveable and comfortable urban environments from the perspective of physiological indicators.

Exploring the Motivations and Personality Traits of Pro-Russian Collaborationists in Ukraine.

This study investigates the personality traits and motivations of collaborationists with Russian invaders in Ukraine. A focus group of individuals who knew collaborationists personally identified 14 collaborationism motives (CMs), which were used to interview 104 probation clients convicted of collaborationism. The study utilized the five-factor model developed by Costa and McCrae to evaluate individual characteristics. Categorical principal component analysis (CATPCA) reduced the 14 CMs to three dimensions: pragmatism, adaptation, and fear. The study found that individual factors promoting collaborationism included pragmatism, ideological adaptation to changing realities, and fear stemming from threats to the life and health of an individual and/or their relatives. The study provides insights into the psychology of collaborationism, which can be useful in designing resocialization programs during the probation period of collaborationists.

IDENTIFICATION OF MOBILITY STRATEGIES FOR SMALL AND URBANIZED BRAZILIAN MUNICIPALITIES

Due to intense urbanization and inadequate planning, various mobility problems have emerged in small Brazilian municipalities. To reverse this scenario, it is necessary to adopt sustainable mobility policies. Thus, this article aims to identify mobility strategies for small and urbanized Brazilian municipalities. Firstly, based on the literature, twenty strategies were identified. Then, experts and managers evaluated these strategies to identify the most suitable ones for small municipalities. Next, the data was analyzed to measure the reliability of the responses, the correlation, and the ranking of the strategies. Finally, the most appropriate strategies were identified using Categorical Principal Component Analysis. The results indicated that the most relevant strategies to be implemented in small Brazilian municipalities are related to policy issues, promoting active transportation, and encouraging public transportation. The findings can improve public policies and transportation planning, with implications for quality of life and sustainable development in small Brazilian municipalities.

From provider to partner? Main elements of the relationship between schools and small‐ and medium‐sized firms in vocational education work placements in the Basque and Navarre regions (Spain)

AbstractThis paper seeks to contribute to the knowledge of the relationship established between firms and vocational education and training (VET) schools around work placements in companies (both standard and extended, so‐called ‘dual’, models) in Spain. An ad hoc survey of 332 small and medium enterprises (SMEs) from two industrial regions was conducted and a categorical principal components analysis was applied to identify the main dimensions or components of those relationships. The main elements found are related to (i) staff selection and social responsibility; (ii) trust, reciprocity and knowledge sharing with schools; and (iii) the organizational culture of the companies. The results obtained illustrate an evolution from a ‘provider‐client’ scheme towards a more strategic partnership between schools and firms, thus contributing new aspects to the literature available on the Spanish case.

Exploring user adoption of online hotel booking platforms: A text mining approach

The growing importance of online hotel booking platforms has revolutionized the hospitality industry, generating new business opportunities and intense competition among service providers This study analyzes user-generated content from online reviews to understand what drives user adoption of online hotel booking platforms. Using topic modeling, categorical principal component analysis, and relative importance ranking, the study identifies key factors like comparison of hotel options and online booking features as crucial for user adoption. Conversely, negative hotel experiences contribute to dissatisfaction. The results offer valuable insights for the hospitality industry, emphasizing the dominance of online booking and price comparison in customer adoption.

Knowledge and Attitudes of Parents and Caregivers in New Delhi to Childhood Hearing Loss and Hearing Services.

Parental and caregiver support is crucial for addressing childhood hearing loss in low and middle-income countries, where a significant burden of disabling hearing loss exists among children. This study aimed to assess the knowledge and attitudes of parents and caregivers regarding childhood hearing loss and available hearing services in both urban and rural settings in Delhi, India. A total of 314 participants were recruited and interviewed using a culturally adapted questionnaire consisting of 26 items, covering topics like biomedical and non-biomedical beliefs, knowledge of otitis media-related hearing loss, identification, and intervention. Statistical analysis of data was performed using Mann Whitney U tests and categorical principal component analysis (PCA). The study found that parents generally had a higher percentage of correct responses compared to caregivers in both urban and rural areas. Categorical PCA revealed variations in responses between fathers, mothers, and caregivers. In the urban group, mothers exhibited higher knowledge and more positive attitudes, while fathers showed lower involvement. In the rural group, while mothers demonstrated relatively higher awareness on all categories compared to fathers, these differences were highly relevant on questions concerning noise exposure, maternal medicine during pregnancy, and communication milestones. The findings emphasize the importance of considering regional and cultural factors when designing programs. Additionally, involving fathers in awareness campaigns and support networks is crucial to achieve comprehensive coverage in addressing childhood hearing loss. In conclusion, this study contributes valuable insights into the preparedness of parents and caregivers for audiology services and the need for contextually appropriate strategies to enhance childhood hearing health services in India.

A novel hybrid feature selection and ensemble-based machine learning approach for botnet detection

In the age of sophisticated cyber threats, botnet detection remains a crucial yet complex security challenge. Existing detection systems are continually outmaneuvered by the relentless advancement of botnet strategies, necessitating a more dynamic and proactive approach. Our research introduces a ground-breaking solution to the persistent botnet problem through a strategic amalgamation of Hybrid Feature Selection methods—Categorical Analysis, Mutual Information, and Principal Component Analysis—and a robust ensemble of machine learning techniques. We uniquely combine these feature selection tools to refine the input space, enhancing the detection capabilities of the ensemble learners. Extra Trees, as the ensemble technique of choice, exhibits exemplary performance, culminating in a near-perfect 99.99% accuracy rate in botnet classification across varied datasets. Our model not only surpasses previous benchmarks but also demonstrates exceptional adaptability to new botnet phenomena, ensuring persistent accuracy in a landscape of evolving threats. Detailed comparative analyses manifest our model's superiority, consistently achieving over 99% True Positive Rates and an unprecedented False Positive Rate close to 0.00%, thereby setting a new precedent for reliability in botnet detection. This research signifies a transformative step in cybersecurity, offering unprecedented precision and resilience against botnet infiltrations, and providing an indispensable blueprint for the development of next-generation security frameworks.

Visitation patterns in a peri-urban natural park: Comparing mountain bikers, runners, walkers and hikers

Urban natural parks are popular destinations for activities such as short walks, longer hikes, mountain biking and running. But how does visitation for these activities vary, and how can information about visitation patterns facilitate management by enhancing social and environmental benefits? Employing a case study approach, we compared the four activities in Serra de Collserola Natural Park, a large popular park close to Barcelona in Spain. When 1788 visitors were surveyed and data analysed using Categorical Principal Components Analysis and Chi-squared tests, clear differences were found in sociodemographics (age, gender, occupation, educational level and where people lived), visitation patterns (frequency, means of transport, group size and composition, when visited, length of visit, and trail use), motivations (active, relaxation, nature, landscapes, socializing, time with family, etc.), and environmental and social impacts, including conflicts among user groups and crowding. This diversity in visitation highlights ways in which management could be better tailored to specific cohorts of visitors. For this specific park it highlighted issues with concentrated use (more resources needed on weekends), how equity of use could be enhanced (promoting running and biking for women), and how conflict could be reduced (by spatial segregation for faster and slower activities). Future surveys could examine emerging issues associated with changes in use since COVID-19, facilities for aging populations, and the spread of e-bikes among others.

Unpacking the invisible complex realities: intersections of gender and marital status in determining the intrinsic vulnerability of smallholder farmers to climate change in Northwestern Ethiopia

ABSTRACT Studies in Ethiopia have shown how climate-induced impacts are experienced differently by male and female farmers, with women being generally more vulnerable. Yet, less is known about how vulnerability to climate-induced risks differs between men and women of different marital statuses Therefore, we applied an intersectionality framework to examine how the level of intrinsic vulnerability is mediated through gender and marital status. Categorical Principal Component Analysis (CAPCA) was used to construct a household heads’ intrinsic vulnerability index. Drawing on a systematic random sample of 1602 respondents and based on logistic regression analysis, the study shows that, while a man’s marital status is a less important factor in determining his level of vulnerability to climate change, it is a key factor in the case of women. Compared to other women, female divorcees and widows are highly likely to be vulnerable to climate change in northwestern parts of Ethiopia. Integrating information about marital relationships, especially the neglected widows and divorcees into the design of interventions may help to better target potential beneficiaries among smallholder farming communities. Our study makes a unique contribution to the intersectionality literature in understanding the diversified experiences of vulnerable rural communities which are susceptible to climate-induced shocks.

Can CATPCA be utilized for spatial modeling? a case of the generation susceptibility of gully head in a watershed

Many spatial modeling methods have emerged; however, they require dependent variables, cannot reflect the relationship between categorical variables and numerical variables, and are limited by the interference of data collinearity. Categorical principal components analysis (CATPCA) has the potential to overcome these issues. Therefore, in order to investigate the suitability of CATPCA for spatial modeling, we conducted a case study based on the generation susceptibility of gully heads was determined, including 2310 gully head and 23 variables. CATPCA was first used in the spatial modeling of gully head generation. The first six principal components retained 76.4% of data trends. The area of the training and validation sensitivity curves were 75.4% and 75.7%, respectively, which reflected good levels. CATPCA can simulate gully head spatial differences, and thus, has great potential for spatial modeling applications. Among the 23 factors, elevation, distance to residential areas, human footprint, lithology, and soil type were identified as the main controlling factors affecting the generation susceptibility of gully heads, with high correlations between them. Low altitudes, close proximity to residential areas, high human footprint, and poor vegetation were associated with high susceptibility. The findings of this study provide a better understanding of the applicability of CATPCA for spatial modeling. CATPCA is a novel solution for spatial modeling strategies that can improve understanding and has great potential for various spatial modeling applications in the future.

Community pharmacists’ readiness to take action to improve working conditions

ObjectiveThe objective of this study was to assess pharmacists’ readiness to take actions resulting in change at the individual level or systemic level. Design/Setting and Participants/Outcome measuresAn online survey was distributed via pharmacy-related social media pages and several state pharmacy association newsletters and e-mail listservs. Fifteen items assessed participants’ readiness to pursue change using a 5-point scale based on the transtheoretical model of behavior change (1 = Do not plan on doing, 2 = Plan to take in the next 6 months, 3 = Plan to take in the next month, 4 = Took in the past 6 months, 5 = Took more than 6 months ago). Categorical principal components analysis (CATPCA) was used as a data reduction technique to identify underlying components of the 15 items assessing readiness to improve working conditions. Component scores for each component were calculated and used as dependent variables in multivariable linear regression models to test associations between the components and sociodemographic and work-related predictors. ResultsCATPCA revealed a 12-item scale with 2 components accounting for 39.97% of the total variance. Pharmacists working in independent pharmacies had a significantly higher willingness than chain pharmacists to pursue actions with potential to result in systemic-level changes, such as contacting or joining an organization to attempt to improve their current working conditions (regression coefficient = 0.623, P < 0.001). Pharmacists working in independent pharmacies had a significantly lower willingness than chain pharmacists to pursue actions resulting in individual-level changes, such as changing their current job or quitting (regression coefficient = −0.485, P < 0.001). ConclusionPharmacists working in national chains may be less willing than independent pharmacists to pursue systemic-level changes owing to fear of discipline from employers. Future research should examine the effect of interventions intended to increase pharmacists’ motivation and readiness to pursue changes to improve working conditions.

Validation of the test for finding word retrieval deficits (WoFi) in detecting Alzheimer's disease in a naturalistic clinical setting

BackgroundDetecting impaired naming capacity contributes to the detection of mild (MildND) and major (MajorND) neurocognitive disorder due to Alzheimer’s disease (AD). The Test for Finding Word retrieval deficits (WoFi) is a new, 50-item, auditory stimuli-based instrument.ObjectiveThe study aimed to adapt WoFi to the Greek language, to develop a short version of WoFi (WoFi-brief), to compare the item frequency and the utility of both instruments with the naming subtest of the widely used Addenbrooke’s cognitive examination III (ACEIIINaming) in detecting MildND and MajorND due to AD.MethodsThis cross-sectional, validation study included 99 individuals without neurocognitive disorder, as well as 114 and 49 patients with MildND and MajorND due to AD, respectively. The analyses included categorical principal components analysis using Cramer’s V, assessment of the frequency of test items based on corpora of television subtitles, comparison analyses, Kernel Fisher discriminant analysis models, proportional odds logistic regression (POLR) models and stratified repeated random subsampling used to recursive partitioning to training and validation set (70/30 ratio).ResultsWoFi and WoFi-brief, which consists of 16 items, have comparable item frequency and utility and outperform ACEIIINaming. According to the results of the discriminant analysis, the misclassification error was 30.9%, 33.6% and 42.4% for WoFi, WoFi-brief and ACEIIINaming, respectively. In the validation regression model including WoFi the mean misclassification error was 33%, while in those including WoFi-brief and ACEIIINaming it was 31% and 34%, respectively.ConclusionsWoFi and WoFi-brief are more effective in detecting MildND and MajorND due to AD than ACEIIINaming.