Final Class Research Articles

Néstor Curbelo: The Latin American Andrew Jenson

Néstor Curbelo: The Latin American Andrew Jenson

RETRACTED: Part II: a step-by-step guide to latent class analysis

Latent Class Analysis (LCA) is an analytical approach for the identification of more homogeneous subgroups within an otherwise dissimilar patient population. In the current paper, Part II, we present a practical step-by-step guide for LCA of clinical data, including when LCA might be applied, selecting indicator variables, and choosing a final class model. We also identify some common pitfalls of LCA, and some related solutions.

Bacteria Community Inhabiting Heterobasidion Fruiting Body and Associated Wood of Different Decay Classes.

The microbiome of Heterobasidion-induced wood decay of living trees has been previously studied; however, less is known about the bacteria biota of its perennial fruiting body and the adhering wood tissue. In this study, we investigated the bacteria biota of the Heterobasidion fruiting body and its adhering deadwood. Out of 7,462 operational taxonomic units (OTUs), about 5,918 OTUs were obtained from the fruiting body and 5,469 OTUs were obtained from the associated dead wood. Interestingly, an average of 52.6% of bacteria biota in the fruiting body was shared with the associated dead wood. The overall and unique OTUs had trends of decreasing from decay classes 1 to 3 but increasing in decay class 4. The fruiting body had the highest overall and unique OTUs number in the fourth decay class, whereas wood had the highest OTU in decay class 1. Sphingomonas spp. was significantly higher in the fruiting body, and phylum Firmicutes was more dominant in wood tissue. The FAPROTAX functional structure analysis revealed nutrition, energy, degradation, and plant-pathogen-related functions of the communities. Our results also showed that bacteria communities in both substrates experienced a process of a new community reconstruction through the various decay stages. The process was not synchronic in the two substrates, but the community structures and functions were well-differentiated in the final decay class. The bacteria community was highly dynamic; the microbiota activeness, community stability, and functions changed with the decay process. The third decay class was an important turning point for community restructuring. Host properties, environmental factors, and microbial interactions jointly influenced the final community structure. Bacteria community in the fruiting body attached to the living standing tree was suppressed compared with those associated with dead wood. Bacteria appear to spread from wood tissue of the standing living tree to the fruiting body, but after the tree is killed, bacteria moved from fruiting body to wood. It is most likely that some of the resident endophytic bacteria within the fruiting body are either parasitic, depending on it for their nutrition, or are mutualistic symbionts.

Constraints on the Genesis of Cobalt Deposits: Part II. Applications to Natural Systems

AbstractIn a companion paper in this issue, the authors reviewed the properties of cobalt, its mineralogy, and the processes that concentrate it to exploitable levels. Using this information and knowledge of the geology of the principal types of cobalt deposits, the present paper assesses the conditions and controls of cobalt transport and deposition and develops/refines plausible models for the genesis of these deposits. Economic cobalt deposits owe their origins to the compatible nature of Co2+, which causes it to concentrate in the mantle, mainly in olivine, and to be released to magmas only after high degrees of partial melting (i.e., to komatiitic and basaltic magmas). Thus, there is a very close association between cobalt deposits and mafic and ultramafic rocks. Magmatic deposits, in which Co is subordinate to Ni, develop through sulfide-silicate liquid immiscibility as a result of the very strong preference of these metals for the sulfide liquid. Predictably, these deposits reach their highest grades where hosted by olivine-rich ultramafic rocks. Approximately 60% of the world’s cobalt resource is of hydrothermal origin and is contained in sediment-hosted copper deposits in the Democratic Republic of the Congo. Using a combination of thermodynamic data and geologic information, we have refined a model in which Co is leached from mafic and ultramafic rocks by oxidized, chloride-rich hydrothermal fluids, derived from evaporation, and deposited in response to decreasing fO2 in carbonaceous sediments that accumulated in intracratonic rift basins. Economic Co deposits also develop as hydrothermal vein systems, in which Co is the primary ore metal. In the only deposits of this type that are currently being exploited (Bou Azzer, Morocco), the source of the Co was an adjacent serpentinized peridotite. The ore fluid was an oxidized, high-salinity brine derived from evaporites, and deposition occurred in response to pH neutralization by the felsic to intermediate igneous host. The final major class of Co deposits is laterite-hosted and develops on olivine-rich ultramafic rocks or their serpentinized equivalents. Our thermodynamic modeling shows that Co is leached from an ultramafic source by mildly acidic fluids as Co2+ and is transported down the laterite profile, eventually concentrating by a combination of adsorption on Mn oxides, incorporation in the structure of absolane (an Mn oxide), and precipitation as heterogenite (HCoO2). The dissolution of cobalt at the surface and its deposition at depth are controlled mainly by pH, which decreases downward; oxygen fugacity, which also decreases downward, has the opposite effect, inhibiting dissolution of cobalt at the surface and promoting it at depth. It is our hope that this introduction to the economic geology of cobalt and the processes responsible for the formation of cobalt-bearing ores will help guide future studies of cobalt ore genesis and strategies for the exploration of this critical metal.

Knowledge of Medical Students about Infant Feeding Practices

Objectives: To compare medical student’s knowledge about breast feeding practices at the induction and exit from medical college. Study design: Comparative study Place and duration of study: Department of Pediatrics, University College of Medicine & Dentistry, The University of Lahore. Study was conducted from January 2019 to December 2019. Material and method: Total 200 students were enrolled, 100 from first year and 100 from final year class. Ethical approval was taken from ethical review committee data was collected on hand filled questionnaire. Results: There were 39.5% males and 60.5% female students. The ANOVA test of the variance was applied to compare the knowledge of first year and final year students. Final year students have a good knowledge regarding timing of initiation and pattern of breastfeeding whereas knowledge regarding advice of pre-lacteal, continuation of breastfeeding up to 6 months and frequency of complementary feeding for initial days was lacking in both first year and final year MBBS students. Conclusion: This study concludes that our curriculum has incorporated adequate knowledge regarding benefits of breast feeding, but the students lack practical knowledge and skills of counseling the parents about avoiding pre-lacteal feeds and promoting complementary feeding, that is also essential for child growth and development. Keywords: Medical college, Curriculum, Exclusive Breast feeding (EBF), complementary feeding.

Transition blues: Sources of Stress among final year medical students approaching graduation

Background: To assess the prevalence of the most common sources of stress among graduating final year medical students and to compare the students on gender and on a relative belonging to a medical profession. Methods: Students from two consecutive batches of final year MBBS were requested to complete a self-developed questionnaire about the various sources of stress. The study commenced from September 2018 to January 2020. Results were entered and analyzed in SPSS version 23. Descriptive statistics and independent samples T-test were used to analyze the data gathered. Results: A total of 194 (102 female, 92 male) final year MBBS class students participated in the study. The three most common sources of stress reported were; dealing with the practical life after graduation; encountering senior doctors as bosses and the highest expectations of their family/friends. Nearly two thirds of students reported high over stress levels. Female students were significantly more likely to report higher overall stress levels. On individual stress related items, females were significantly more likely to be stressed about the practical life ahead, about not feeling capable enough to deal with human life, about actual encounter with patients, and about encounter with senior doctors as their bosses. Students who had a parent or a sibling who was a doctor were significantly more likely to be stressed about the highest expectation of family friends. Conclusions: A significant number of final year medical students are highly stressed about the transition to working life. Female medical students are more likely to report overall stress as well as on particular stress related aspects of practical life and undergraduate training. Mesh terms: Psychological Stress, Medical students, Mental health

“Nga-wayang”: A New Dance Creation from a Globalized Sociocultural Environment

This scientific article aims to discuss a new dance creation entitled “Nga-wayang”, whose creation idea depicts the character Gatot Kaca in a two-dimensional wayang kulit show. No one has worked on a dance that was transformed from a two-dimensional wayang kulit performance into a new three-dimensional dance creation. The creation of the “Nga-wayang” dance uses qualitative methods. The creation process is carried out in stages: ngarencana, nuasen, makalin, nelesin, and ngebah. The search for ideas was carried out using participatory observation techniques and in-depth interviews with related parties. The deepening of the character of Gatot Kaca's character is carried out through library research and discography. The process of creating this new dance creation is carried out using transformation theory and aesthetic theory. The cultivation of this work of art has resulted in a new dance creation entitled “Nga-wayang”. As a new dance creation, “Nga-wayang” is presented full of new ideas based on the influence of the art ecosystem in the influence of globalization in terms of poses, variety of movements, techniques, dynamics, tempo, and level play based on multimedia such as LCD projectors, LED lights, and smoke. The process of composing this dance has been carried out since this material was developed as a final choreography class assignment, the presentation of which has been continuously refined. “Nga-wayang” is a new dance creation that departs from the experience of the cultural environment in the era of globalization, namely the ecosystem as an art reinforcement.

Self-supervised Short-text Modeling through Auxiliary Context Generation

Short text is ambiguous and often relies predominantly on the domain and context at hand in order to attain semantic relevance. Existing classification models perform poorly on short text due to data sparsity and inadequate context. Auxiliary context, which can often provide sufficient background regarding the domain, is typically available in several application scenarios. While some of the existing works aim to leverage real-world knowledge to enhance short-text representations, they fail to place appropriate emphasis on the auxiliary context. Such models do not harness the full potential of the available context in auxiliary sources. To address this challenge, we reformulate short-text classification as a dual channel self-supervised learning problem (that leverages auxiliary context) with a generation network and a corresponding prediction model. We propose a self-supervised framework, Pseudo-Auxiliary Context generation network for Short-text Modeling (PACS) , to comprehensively leverage auxiliary context and it is jointly learned with a prediction network in an end-to-end manner. Our PACS model consists of two sub-networks: a Context Generation Network (CGN) that models the auxiliary context’s distribution and a Prediction Network (PN) to map the short-text features and auxiliary context distribution to the final class label. Our experimental results on diverse datasets demonstrate that PACS outperforms formidable state-of-the-art baselines. We also demonstrate the performance of our model on cold-start scenarios (where contextual information is non-existent) during prediction. Furthermore, we perform interpretability and ablation studies to analyze various representational features captured by our model and the individual contribution of its modules to the overall performance of PACS, respectively.

Belief Combination of Classifiers for Incomplete Data

Data with missing values, or incomplete information, brings some challenges to the development of classification, as the incompleteness may significantly affect the performance of classifiers. In this paper, we handle missing values in both training and test sets with uncertainty and imprecision reasoning by proposing a new belief combination of classifier (BCC) method based on the evidence theory. The proposed BCC method aims to improve the classification performance of incomplete data by characterizing the uncertainty and imprecision brought by incompleteness. In BCC, different attributes are regarded as independent sources, and the collection of each attribute is considered as a subset. Then, multiple classifiers are trained with each subset independently and allow each observed attribute to provide a sub-classification result for the query pattern. Finally, these sub-classification results with different weights (discounting factors) are used to provide supplementary information to jointly determine the final classes of query patterns. The weights consist of two aspects: global and local. The global weight calculated by an optimization function is employed to represent the reliability of each classifier, and the local weight obtained by mining attribute distribution characteristics is used to quantify the importance of observed attributes to the pattern classification. Abundant comparative experiments including seven methods on twelve datasets are executed, demonstrating the out-performance of BCC over all baseline methods in terms of accuracy, precision, recall, F1 measure, with pertinent computational costs.

Site classification of seismic recording stations of Garhwal region of earthquake early warning system for Uttarakhand, India

Site classification is vital to carry out seismic hazard analysis of a region and get the damage patterns caused by earthquakes. In the present study, the ground motion of earthquakes recorded from 2015 to 2019 at 84 sites of the seismic network array of Earthquake Early Warning System for Uttarakhand are analyzed for site classification purposes. The predominant period from the mean horizontal to vertical spectral ratio curves was estimated. The classification schemes devised by Japan Road Association and National Earthquake Hazards Reduction Program are applied to classify the sites. Along with this, two other site classification indexes schemes are also applied for classification purposes. Data winnowing techniques are used over the ground motion records to pick out desirable quality records. Conclusively, the site class with the highest recurrence rate amongst the used methods is selected as the final class for that particular site. The effect of magnitude, distance and depth on horizontal to vertical spectral ratio are described and concluded that these factors do not significantly affect the ratio curves. The average horizontal to vertical spectral ratio curves obtained for all the sites matches well with the existing literature. The classification of a few sites are verified from the classification done by other methods in recent studies. It is evident from the results that the classification done in this study matches well with them.

Q&A: Use of Hip Motions When Rating Total Hip Replacement

QUESTION: When rating impairment after total hip replacement using the AMA Guides to the Evaluation of Permanent Impairment (AMA Guides), Sixth Edition, I know that if using range of motion (ROM) as a stand-alone method, one would add the percentage lower extremity impairments (LEIs) for each of the six hip motions, and the total is the impairment rating. When using the diagnosis-based impairment method, I have always assumed that if all motion impairments are mild, that is a good result, and I have selected class 2 in Table 16-4, Hip Regional Grid (6th ed, 515). However, I read the report of another physician who, using the same diagnosis-based method, noted mild deficits in all six hip motions, added the percentage impairments for each, and selected class 3, a fair result with a higher rating. Whose logic is correct?ANSWER: Instructions at the top of the right column of the AMA Guides, Sixth Edition, page 543, state the following:Because total hip replacement is listed in Table 16-4, Hip Regional Grid (6th ed, 515), ROM would only rarely, if ever, be used as the rating method following this surgery.Instead, ROM is often used as one factor in physical examination adjustment for grade selection. Although not explicitly stated in the AMA Guides, less commonly, it may be one factor in class selection, eg, when the class criteria for a diagnosis or surgical procedure (such a total hip replacement) reference a good, fair, or poor result.Instructions for how to use ROM to select a class appear in the sixth edition on page 548 in step 4. After determining the total impairment of a joint due to motion loss(es), go to Table 16-25, (6th ed, 550)” … to classify the severity of the lower extremity motion measurements in the ICF [International Classification of Functioning, Disability, and Health] format.” For example, 20% LEI due to motion loss(es) of a joint is classified as moderate severity and class 2, whereas 30% LEI would be classified as severe and class 3. The classification from Table 16-25, combined with other outcome data from a total hip replacement, would be used to determine whether there was a good, fair, or poor result, corresponding to class 2, 3, and 4, respectively. Grade modification would be done following class selection. Table 16-25 shows how one determines whether joint motion loss is mild, moderate, severe, or very severe in Table 16-7, Physical Examination Adjustment (6th ed, page 517). However, to avoid “double-dipping” data, if ROM was used to select the class, it would not be used again in physical examination adjustment.Returning to the question posed, if there were mild impairment for each of the six hip motions per Table 16-24, the total would be 5% × 6 = 30% LEI. Per Table 16-25, this is severe (class 3) impairment. However, ROM is only one factor in determining the outcome of a total hip replacement, and the final class could be 2, 3, or 4, depending on whether there was a good, fair, or poor result, respectively.

A Case Study of University Students’ Performance in Arabic and Islamic Culture Courses Before and During the Pandemic (Face-to-Face and Online Instruction)

Abstract: In the present study, we examined whether students’ academic success in courses devoted to Arabic and Islamic culture changed when the familiar face-to-face delivery format (before the Covid-19 pandemic) was discarded in favor of an online synchronous delivery format (during the pandemic). The final class grades of students enrolled in one of four courses in a sequence devoted to Arabic culture and religion were compared while holding constant the variable instructor. The ability of early performance indicators to predict final class grades was also examined to assess whether there were differences between instructional deliveries. Superior performance and lower failure rates were observed online for courses at the beginning of the sequence, but not at the end of the sequence. These findings suggest that the impact of instructional delivery might vary depending on the students’ accumulated academic experience.

Empirical research methods department: Mission, learnings, and future plans

Empirical research methods department: Mission, learnings, and future plans

Curvature-Driven Deformable Convolutional Networks for End-To-End Object Detection

Recently, deformable convolution networks have shown the superior performance in object detection due to its ability to adapt to the geometric variations of object. These methods learn the offset fields under the supervision of localization and recognition. Nevertheless, the spatial support of these networks may be inexact because the offsets are learned implicitly via extra convolutional layer. In this work, we present curvature-driven deformable convolutional networks (C-DCNets) that adopt explicit geometric property of the preceding feature maps to enhance the deformability of convolution operation and make the networks easier to focus on pertinent image region. To be consistent with postprocessing technology of object detection, we multiply the class prediction probability by the similarity of predicted boxes and ground truth boxes as the final class prediction probability and substitute it into the binary cross entropy loss function. The obtained loss function correlates the bounding box regression and classification. Experimental results on PASCAL VOC and COCO data set show that C-DCNets-based YOLOv4 with the proposed loss function outperforms state-of-the-art algorithms.

A lecture series – neurosurgery in an Irish Medical School without an associated neurosurgical Centre

Background: Neurosurgery is a surgical specialty that is felt to be under-represented in its teaching and education at an undergraduate level in Irish medical schools, particularly in those which are not attached to a specialist neurosurgical centre. We looked at exposure to neurosurgery among undergraduate students by organising two neurosurgical lecture days for the 2020/2021 final year medical class of National University of Ireland Galway (NUIG), an Irish medical school which is not associated with a neurosurgical centre. This study sought to ascertain students’ engagement with and, respectively, desire for a greater emphasis on common subjects within neurosurgery as part of an undergraduate curriculum, specifically in a medical school without an associated neurosurgical centre. Methods: The lecture series was organised by a final year medical student in NUI Galway in conjunction with the National Neurosurgical Centre, Beaumont Hospital. The lectures took place over the course of two separate lecture days on Saturday October 10th and Saturday November 14th, respectively. Both lecture days were broadcast virtually, in compliance with COVID-19 guidelines, to the NUIG 2020/2021 final year medical class and the content covered therein composed part of the examinable syllabus for their final year medical exams. All class members were provided with an online pre-lecture survey prior to the initial lecture day and two post-lecture surveys, one at the end of each respective lecture day. Results: 194 final year medical students from a class of 205 attended over the course of the two given lecture days. Of the 148 students that completed the pre-lecture survey only 13 students had previously attended a neurosurgical lecture or conference. Of the 194 attendees, there were 116 students who completed the final post-lecture survey, 62% of whom agreed that they would like to receive further teaching and clinical exposure to neurosurgery as part of their undergraduate medical education.

Health Literacy Among German Health Professionals-To-Be - Exploratory Pilot Study

Health literacy is defined as the ability to collect, understand, evaluate and use health information. Previous studies have shown that the German population has only an average level of health literacy. Health professionals such as nurses, doctors or midwives need to be health literate themselves to promote patients' health literacy, as well as to protect and promote their own health. So far, there is a lack of research about health literacy among health professionals in Germany. The objective of this study was to assess the health literacy of health professionals. The study involved 583 participants from the final year classes of the DHBW Stuttgart and dual cooperation partners (vocational schools and academic teaching hospitals). These included graduates of nursing, midwifery and therapy (physiotherapy/logo-/occupational therapy), as well as management in health care and medical students in their practical year. The questionnaire HLS-EU-Q16 was used to record health literacy. By means of 16 questions, the participants rated on a 4-point scale how easy or difficult it was for them to find, understand, assess and implement health-related information. The study showed that only 30.1% of the participants had sufficient health literacy; 48.9% of the participants showed problematic health literacy, 21.0% inadequate health literacy. The analysis of the individual questions showed that, in almost all areas, paticipants reported more problems in finding, understanding, assessing and implementing health-related information than respondents in other European countries. The present study shows that young health professionals have limited health literacy. Further research is needed to obtain a more comprehensive and differentiated picture of health literacy among health professionals. Health literacy skills and competencies should be systematically introduced in health professions-related study curricula, as well as in bedside training.

A convolutional deep learning model for improving mammographic breast-microcalcification diagnosis

This study aimed to assess the diagnostic performance of deep convolutional neural networks (DCNNs) in classifying breast microcalcification in screening mammograms. To this end, 1579 mammographic images were collected retrospectively from patients exhibiting suspicious microcalcification in screening mammograms between July 2007 and December 2019. Five pre-trained DCNN models and an ensemble model were used to classify the microcalcifications as either malignant or benign. Approximately one million images from the ImageNet database had been used to train the five DCNN models. Herein, 1121 mammographic images were used for individual model fine-tuning, 198 for validation, and 260 for testing. Gradient-weighted class activation mapping (Grad-CAM) was used to confirm the validity of the DCNN models in highlighting the microcalcification regions most critical for determining the final class. The ensemble model yielded the best AUC (0.856). The DenseNet-201 model achieved the best sensitivity (82.47%) and negative predictive value (NPV; 86.92%). The ResNet-101 model yielded the best accuracy (81.54%), specificity (91.41%), and positive predictive value (PPV; 81.82%). The high PPV and specificity achieved by the ResNet-101 model, in particular, demonstrated the model effectiveness in microcalcification diagnosis, which, in turn, may considerably help reduce unnecessary biopsies.

Impact of COVID Pandemic and Hybrid teaching on Final year MBBS students' End of clerkship Exam performance.

ABSTRACTBackground & Objectives:The novel coronavirus (SAR-CoV-2) pandemic has revolutionized medical education worldwide. Most medical schools have adopted the online teaching and assessments. Students attending modified clerkships and assessments under the stress of the pandemic, perform and score differently when compared to normal clerkships. We aimed to identify the impact of COVID-19 on final year MBBS students’ EOC (End of Clerkship) examination by comparing them with their scores prior to the COVID and with scores of the previous final year.Methods:This cross-sectional study was conducted at Shifa College of Medicine. Final year MBBS students’ scores of years 2019 and 2020 were included. Students’ EOC MCQ and OSCE scores were compared in pre-COVID and COVID affected rotations of the same year and with the previous year (2019). Data were analyzed in SPSS version 21, means scores were calculated, and one-way ANOVA was applied. Pearson correlation was calculated for correlation assessment of MCQ and OSCE scores.Results:There were 118 students. The mean EOC, OSCE, and MCQ scores in rotations one to four were 72.8±6.4, 73.3± 8.1, 71.6± 7.4, 72.7± 6.7 and 44.4± 8, 47.2± 8.4, 46.1± 8.2, 48.8± 8.1, respectively. One-way ANOVA results before and after COVID lockdown were statistically insignificant (p=0.3) for OSCE and significant for MCQ in the final year class of 2020 (p=0.001). The Pearson correlation assessment between MCQ and OSCE scores (n=416) had a significant positive correlation (r=0.42, p=0.000). The overall comparison between scores of the final year class of 2019 and 2020 showed significant improvement in Surgery and Obstetrics/Gynae scores in 2020.Conclusion:During the COVID pandemic, the final year students’ performance in EOC MCQ and OSCE over all remained unaffected.

NIMG-25. IMPROVING THE NONINVASIVE CLASSIFICATION OF GLIOMA GENETIC SUBTYPE WITH DEEP LEARNING AND DIFFUSION-WEIGHTED IMAGING

Abstract In contrast to the WHO 2016 guidelines that use genetic alterations to further stratify patients within a designated grade, new recommendations suggest that IDH mutation status, followed by 1p19q-codeletion, should be used before grade when differentiating gliomas. Although most gliomas will be resected and their tissue evaluated with genetic profiling, non-invasive characterization of genetic subgroup can benefit patients where surgery is not otherwise advised or a fast turn-around is required for clinical trial eligibility. Prior studies have demonstrated the utility of using anatomical images and deep learning to distinguish either IDH-mutant from IDH-wildtype tumors or 1p19q-codeleted from non-codeleted lesions separately, but not combined or using the most recent recommendations for stratification. The goal of this study was to evaluate the effects of training strategy and incorporation of Apparent Diffusion Coefficient (ADC) maps from diffusion-weighted imaging on predicting new genetic subgroups with deep learning. Using 414 patients with newly-diagnosed glioma (split 285/50/49 training/validation/test) and optimized training hyperparameters, we found that a 3-class approach with T1-post-contrast, T2-FLAIR, and ADC maps as inputs achieved the best performance for molecular subgroup classification, with overall accuracies of 86.0%[CI:0.839,1.0], 80.0%[CI:0.720,1.0], and 85.7%[CI:0.771,1.0] on training, validation, and test sets, respectively, and final test class accuracies of 95.2%(IDH-wildtype), 88.9%(IDH-mutated,1p19qintact), and 60%(IDHmutated,1p19q-codeleted). Creating an RGB-color image from 3 MRI images and applying transfer learning with a residual network architecture pretrained on ImageNet resulted in an 8% averaged increase in overall accuracy. Although classifying both IDH and 1p19q mutations together was overall advantageous compared with a tiered structure that first classified IDH mutational status, the 2-tiered approach better generalized to an independent multi-site dataset when only anatomical images were used. Including biologically relevant ADC images improved model generalization to our test set regardless of modeling approach, highlighting the utility of incorporating diffusion-weighted imaging in future multi-site analyses of molecular subgroup.

Developing scoring functions for soil quality to assess land suitability for irrigated wheat in Southern Algeria

AbstractEfficient land‐use management could be optimized through appropriate advice regarding land suitability and achieving a reasonable yield while preserving soil and environmental conditions. The main objective of this study was to determine and define those locations capable of wheat cultivation with irrigation in the municipality of N’goussa (Southern Algeria). Expert opinion (EO) method was used for selecting minimum soil data set (MDS‐EO), where six influencing soil properties (sand, pH, salinity, organic matter, gypsum and lime) and other three key parameters (slope, land use/land cover, and proximity to roads and electricity network) were considered important for irrigated wheat cultivation in the study area. Indicators were weighted according to principal component analysis (PCA) loadings and summed. PCA revealed that the selected nine indicators are appropriate for accounting 73.63% of the total variance. Two maps of land suitability were created here (expressed as a soil quality index within a range from 0 to 1) from multivariate analysis and already known standard scoring functions (SSFs) by using two data sets (pure and scored). The distribution of soil quality index (IQI) showed that above 70% of the land was moderately (MS) to highly suitable (HS) for irrigated wheat. The estimation accuracy of soil quality classes has been evaluated using random forests (RF), having as a dependent variable the final class of land suitability used in each map. It was determined that estimation accuracy could reach 60% (pure data set) and 65% (scored data set) at 0.5 cut‐off value for soil quality classes. Final prioritizing according to ranks of sensitivity index (SI) and efficiency ratio (ER) showed that IQI obtained from scored data set is the most suitable approach in soil quality assessment of arid areas. Therefore, it could be applied in southern Algeria for land management, integrated planning and environmental assessments.