Prediction of Urinary Tract Infection Using Machine Learning Algorithms

In sustainable and competitive business management, it is crucial for organizations to consider organizational change and transformational leadership in human resource (HR) management to adapt to the changes in their environment. This capability enables large-scale enterprises to maintain their presence in an increasingly competitive environment through enhanced management capacity. Enterprises that adopt transformational leadership in HR management must equip leadership candidates with competencies such as creating a shared vision, providing appropriate role models, encouraging the adoption of group goals, meeting high performance expectations, and providing individual support and the development of intellectual stimulation. By identifying potential leadership candidates using a decision support model, the necessary competencies can be developed through in-service training and experiential learning during their careers. Innovative and effective approaches to identifying leadership candidates can be developed by analyzing complex big data using advanced artificial intelligence (AI) techniques. In this article, a forecast model using machine learning (ML) algorithms for a human resource management career planning approach was developed for the Turkish Post Corporation (PTT) and it was tested to predict potential leadership candidates by analyzing the big data of 5000 employees. The Turkish Post Corporation ML algorithms were applied to 100 randomly selected data points using the k-Nearest Neighbor (kNN), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM) algorithms to predict the types of titles held by the staff employed at PTT. The kNN, GB, RF, and SVM algorithms achieved accuracy rates of 96%, 91%, 73%, and 41%, respectively. The case study results indicate that promotion decisions in large-scale and rooted enterprises can be effectively modeled using big data and ML algorithms, highlighting significant potential for HR management and leadership development practices in the public sector.

Fusion of diverse data sources for flood extent mapping and risk assessment in Sindh: A comparative study of inundation mapping approaches

MANAGEMENT OF URINARY TRACT INFECTIONS: HISTORICAL PERSPECTIVE AND CURRENT STRATEGIES: PART 2— MODERN MANAGEMENT

Abstract. Hartoni, Siregar VP, Wouthuyzen S, Agus SB. 2021. Object based classification of benthic habitat using Sentinel 2 imagery by applying with support vector machine and random forest algorithms in shallow waters of Kepulauan Seribu. Biodiversitas 23: 514-520. Benthic habitats have very high complexity and are home to many types of aquatic organisms. Benthic habitats have various functions, including habitat for flora and fauna, sediment traps, nursery areas, and foraging areas for aquatic fauna that are susceptible to damage due to human activities or natural factors. Therefore, more accurate spatial information is needed. The purpose of this study was to examine the ability of object-based classification techniques for mapping shallow waters benthic habitats using Sentinel 2A imagery. The two classification algorithms used are support vector machine (SVM) and random forest (RF). The input image layer (IIL) used for classification is the natural color band (Band 432). The results showed that the SVM and RF classification algorithms could classify eight classes of benthic habitats. The overall accuracy (OA) of the SVM algorithm is 65%, while the RF accuracy is 67%, with kappa values of 0.59 and 0.60, respectively. The significant test applied to Sentinel 2 images with SVM and RF algorithms for benthic habitats has a Z test value of-0.41. These results indicate that the classification results between the SVM and RF algorithms are not significantly different.

647 Background: The primary treatment of most mNSTC is chemotherapy followed by surgery if the residual disease (RD) is >1 cm. However, conventional imaging lacks the specificity to characterize the tissue, often leading to overtreatment. This study hypothesizes that integrating CT-driven radiomics features with plasma miR371 and miR375 will enhance the predictive accuracy of Machine Learning (ML) models to predict teratoma, viable germ cell (vGCT) and fibrosis/necrosis (F/N) in mNSTC patients with RD. Methods: 111 lesions from52 patients, including residual teratoma (n=57), F/N (n=33), vGCT (n=10), and additional seminoma (n=11) for training purposes were included, split into training (N=78) and test cohorts (N=33). Lesions were lymph nodes (n=87), lung (n=21), and brain (n=3) with a median size of 1.6 cm (Q1-Q3 interval=1.2-2.73 cm). 3D Slicer version 5.6.1 was used to segment the RD > 1 cm (short axis) and extract radiomics features. Plasma miRNA levels before resection were measured by RT-PCR. Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), and CatBoost (CB) ML models were evaluated to define the operating characteristics of radiomics alone (R-only) and in combination with miR371 (371) and/or miR375 (375) levels in predicting teratoma, vGCT and F/N. Results: For predicting teratoma, the best models were RF (R+375 and R+371+375), CB (R+371+375), and GB (R+371 and R+371+375). While adding miR371 or miR375 to R-only slightly improved AUC across models, the best results were achieved with the R+375+371 dataset. CB achieved AUCs ranging from 0.94 to 0.97 in training and 0.81 to 0.93 in test sets, with its highest AUC of 0.93 (95% CI: 0.78-0.97) on the R+375+371 dataset to differentiate all three classes. Similarly, GB demonstrated strong performance, achieving its highest AUC of 0.93 (95% CI: 0.79-0.96) on the R+375+371 dataset (Table). Conclusions: Integration of plasma miR371, miR375 and radiomics improved accuracy of predicting histologies across all ML models. These methods could be used to characterize the histology of RD in mNSTC patients to better inform treatment decisions. Further refinement, including incorporation of histological findings of the primary tumor, will be reported. AUC values of different ML algorithms on training and test sets. TRAINING SET TEST SET Model ±SD R R+375 R+371 R+375+371 Model (95% CI) R R+375 R+371 R+375+371 RF 0.93±0.05 0.95±0.04 0.95±0.03 0.96±0.04 RF 0.8(0.59-0.89) 0.85(0.72-0.93) 0.87(0.76-0.95) 0.91(0.78-0.95) SVM 0.84±0.06 0.84±0.09 0.89±0.11 0.89±0.09 SVM 0.72(0.54-0.80) 0.74(0.56-0.82) 0.83(0.69-0.92) 0.84(0.76-0.94) GB 0.94±0.04 0.91±0.08 0.95±0.05 0.97±0.03 GB 0.84(0.61-0.96) 0.89(0.77-0.97) 0.89(0.79-0.96) 0.93(0.79-0.96) CB 0.95±0.03 0.94±0.03 0.94±0.04 0.97±0.03 CB 0.81(0.6-0.93) 0.86(0.73-0.94) 0.89(0.78-0.97) 0.93(0.78-0.97)

Evapotranspiration (ET) is a vital variable for land‐atmosphere interactions that links surface energy balance, water, and carbon cycles. The in situ techniques can measure ET accurately but the observations have limited spatial and temporal coverage. Modeling approaches have been used to estimate ET at broad spatial and temporal scales, while accurately simulating ET at regional scales remains a major challenge. In this study, we upscale ET from eddy covariance flux tower sites to the regional scale with machine learning algorithms. Five machine learning algorithms are employed for ET upscaling including artificial neural network, Cubist, deep belief network, random forest, and support vector machine. The machine learning methods are trained and tested at 36 flux towers sites (65 site years) across the Heihe River Basin and are then applied to estimate ET for each grid cell (1 km × 1 km) within the watershed and for each day over the period 2012–2016. The artificial neural network, Cubist, random forest, and support vector machine algorithms have almost identical performance in estimating ET and have slightly lower root‐mean‐square error than deep belief network at the site scale. The random forest algorithm has slightly lower relative uncertainty at the regional scale than other methods based on three‐cornered hat method. Additionally, the machine learning methods perform better over densely vegetated conditions than barren land or sparsely vegetated conditions. The regional ET generated from the machine learning approaches captured the spatial and temporal patterns of ET at the regional scale.

ObjectiveEnhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice.MethodsResearches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized.ResultsBased on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics.ConclusionResearchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

In order to precisely manage and control rural credit risks, this study delved deeply into the assessment model predicated on machine learning algorithms. During the data collection phase, 10000 copies of farmers' credit records spanning nearly five years were procured from financial institutions such as rural credit cooperatives and agricultural banks, encompassing over 20 characteristic data items including age, income, land area, and past loan repayment situations. After data cleansing to handle missing values and outliers, the training set and test set were partitioned in a 7:3 ratio. The logical regression, decision tree, random forest, and support vector machine algorithms were employed for modeling. The logical regression optimized parameters via the gradient descent method; the decision tree selected splitting attributes based on information gain; the random forest constructed 100 decision trees and conducted comprehensive voting; the support vector machine mapped data to a high-dimensional space through kernel functions to seek the optimal classification hyperplane. It was experimentally discovered that the random forest algorithm exhibited optimal performance, with an astonishingly high accuracy rate of 85% on the test set, a recall rate of 82%, and an F1 value reaching 0.83, significantly outperforming other algorithms. This model provides robust support for rural credit decision-making by financial institutions, holds the promise of reducing the non-performing loan rate, and propels the healthy development of rural finance.

Background and Aim: The most common pediatric infection is urinary tract infection (UTI). Neonates who suffer from neonatal sepsis remain among the most vulnerable to death and morbidity, especially in developing countries. The present study aimed to determine the prevalence, etiology, and susceptible antimicrobial agents of UTIs among neonates with clinical sepsis. Patients and Methods: This cross-sectional study was carried out on 128 sepsis diagnosed neonates admitted to the Department of Pediatrics, Microbiology and Infectious Diseases, Niazi Welfare Teaching Hospital, Sargodha from September 2021 to May 2022. All neonates aged ≤ 28 days diagnosed of clinical sepsis and consent provided by their parents/guardian were enrolled. An interviewed based questionnaire was used for data collection from neonate’s parents or caregiver. The questionnaire included information on the socio-demographics and clinical presentations of neonates, as well as maternal history and laboratory examinations. The history is taken from prenatal to postnatal, including birth weight analysis and birth conditions. Neonates underwent general and local examinations to determine their state of consciousness, vital signs, and weight. We examined the abdomen (for tenderness and abdominal distention) and the nervous system (to elicit suckling and motor reflexes). A hematological analyzer was used to test the full blood picture (FBP). SPSS version 27 was used for data analysis. Results: Of the total 128 neonates, there were 76 (59.4%) females and 52 (40.6%) male neonates. The overall mean age was 6.4 ± 0.36 days with majority of neonates 112 (87.5%) were < 7 days. The low birth weight and low Apgar score were found in 13 (10.2%) and 16 (12.5%) neonates respectively. Of all neonates, the failure to breastfeed was found in 26 (20.3%) being the most common clinical symptom followed by an increase in breathing rate 21 (16.4%), diarrhea 19 (14.8%), gritting 16 (12.5%), altered mental status 12 (9.4%), and jaundice 11 (8.6%). The incidence of UTI was 20.3% (n=26). The prevalence of separated bacterial agents such as Klebsiella pneumonia and Enterobacter spp. was 61.5% (n=16) and 38.5% (n=10) respectively. Ciprofoxacin and amikacin were both effective against 90% and 60% of the bacterial isolates, respectively. The UTI potential predictors involved prolong labour, positive leucocyte esterase test, Low Apgar score, and positive urine nitrite test. Conclusion: The present study found that the prevalence of UTI was 20.3%. Failure to breastfeed was the most common clinical symptom followed by an increase in breathing rate, diarrhea, gritting, altered mental status, jaundice, convulsion, and cyanosis. Klebsiella pneumonia and Enterobacter spp. were the most prevalent separated bacterial agents. Ciprofoxacin and amikacin were both effective in bacterial isolates. Keywords: UTI, Clinical sepsis, Neonates, Antibiotic sensitivity

India is ranked 120th among 122 countries globally in WaterAid’s water quality index. Regular water quality monitoring is essential to determine which inland water bodies are experiencing depreciating water quality. Long-term trends have been obtained using satellite remote sensing, necessitating multiple image analysis. The computational burden of processing numerous satellite images can be reduced using Google Earth Engine’s (GEE) cloud computing capabilities. Thorough research was conducted to determine the global spatio-temporal and biochemical factors impacting surface water quality (WQ). The public availability of geospatial datasets and free access to cloud-based geo-computing platforms such as GEE are widely used for spatio-temporal mapping, global surface water monitoring, water quality parametric variation, and real-time forecasting. Many researchers have recently focused on improving data mining and machine learning algorithms to accurately deal with image classification and predictive problems for crop identification and monitoring. In the present study, we propose to define band spectral ratios, spectral band equations, and empirical models for water quality parameters. A few neural network models will be used to (i) query and pre-process satellite earth observations that coincide with the study area, (ii) extract the spectra, and (iii) use spectral band wavelength charts, time-series charts, spatial-distribution maps, and the development of an online dashboard application to visualize the results graphically upon using integrated Landsat (8,9), Sentinel-2A/B and PlanetScope satellite data for the pre-monsoon and post-monsoon seasons in 2023 in the pan-India region. MODIS-TERRA provides LST spatial-temporal monitoring. In this, we have assessed and compared the performance of CART, SVM, and RF ML algorithms. We found that RF outperforms CART and SVM algorithms in the GEE platform with PlanetScope data (80.71% overall accuracy (OA) with Kappa 0.89) and also with the integration of PlanetScope and Sentinel-2A/B data (OA = 85.53%, Kappa 0.91). But CART outperforms RF and SVM algorithms with Sentiel-2A/B data (OA = 81.59%, Kappa 0.85). The SAM technique, spectral feature fitting, continuum band removal, and other band-spectral ratio techniques are employed for quantitative hyperspectral data analysis. Specifically, the performance metrics of XGBoost and SGD for both Chl-a (R^2 = 0.818) and Turbidity (R^2 = 0.815) models exhibited robust accuracy. We have also developed a Google API-based JavaScript code that can be tested under complex coastal shores, challenging inundation and variable climatic conditions. Our method provides the end-to-end cloud computing workflow shown in this research, considering cost and computational efficiency for timely information delivery.Keywords: Classification and Regression Trees (CART), Chlorophyll-a (conc), Color dissolved organic matter (CDOM), Composite water management index (CWMI), Environmental Mapping and Analysis Program (EnMAP), Land Surface Temperature (LST), Machine Learning (ML), Random Forest (RF), Spectral Angle Mapper (SAM), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Total Suspended Solids (TSS), Water Quality (WQ), XGBoost (Extreme Gradient Boosting).

Diabetic Retinopathy (DR) is a microangiopathic complication of diabetes mellitus that can cause visual impairment to permanent blindness. Early detection of DR is essential to prevent disease progression, but conventional methods require time, cost, and expertise that are not always available. This study aims to compare the performance of the Random Forest (RF) and Support Vector Machine (SVM) algorithms in DR classification based on texture features extracted from retinal fundus images. The dataset used consists of 3,000 retinal fundus images obtained from the Kaggle platform, divided into 2,400 training data and 600 test data. Image preprocessing includes conversion to grayscale, resizing to a resolution of 128×128 pixels, and normalization. Feature extraction is performed using a combination of Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) to produce a 14-dimensional feature vector. Performance evaluation uses accuracy, precision, recall, F1-score, ROC curve, and 5-fold cross-validation metrics. The results showed that Random Forest significantly outperformed SVM with an accuracy of 96% compared to 64%, an AUC value of 0.99 compared to 0.72, and an average cross-validation accuracy of 94.5% compared to 63.42%. Random Forest also showed balanced performance in both classes with precision, recall, and F1-score of 0.96, while SVM experienced classification imbalance especially in the disease class. This study proves that Random Forest is a more optimal algorithm for an automatic DR detection system based on fundus image texture features and can support increasing the accessibility of DR screening in areas with limited specialist medical personnel.

To analyze the effect of macular choroidal thickness (MCT) and peripapillary choroidal thickness (PPCT) on the classification of obese and healthy children by comparing the performance of the random forest (RF), support vector machine (SVM), and multilayer perceptrons (MLP) algorithms. Fifty-nine obese children and 35 healthy children aged 6 to 15 years were studied in this prospective comparative study using optical coherence tomography. MCT and PPCT were measured at distances of 500 μm, 1,000 μm, and 1,500 μm from the fovea and optic disc. Three different feature selection algorithms were used to determine the most prominent features of all extracted features. The classification efficiency of the extracted features was analyzed using the RF, SVM, and MLP algorithms, demonstrating their efficacy for distinguishing obese from healthy children. The precision and reliability of measurements were assessed using kappa analysis. The correlation feature selection algorithm produced the most successful classification results among the different feature selection methods. The most prominent features for distinguishing the obese and healthy groups from each other were PPCT temporal 500 μm, PPCT temporal 1,500 μm, PPCT nasal 1,500 μm, PPCT inferior 1,500 μm, and subfoveal MCT. The classification rates for the RF, SVM, and MLP algorithms were 98.6%, 96.8%, and 89%, respectively. Obesity has an effect on the choroidal thicknesses of children, particularly in the subfoveal region and the outer semi-circle at 1,500 μm from the optic disc head. Both the RF and SVM algorithms are effective and accurate at classifying obese and healthy children.

Predictive analytics in customer behavior: Anticipating trends and preferences

ObjectiveTo build a machine learning (ML) prediction model for prostate cancer (PCa) from transrectal ultrasound video clips of the whole prostate gland, diagnostic performance was compared with magnetic resonance imaging (MRI).MethodsWe systematically collated data from 501 patients—276 with prostate cancer and 225 with benign lesions. From a final selection of 231 patients (118 with prostate cancer and 113 with benign lesions), we randomly chose 170 for the purpose of training and validating a machine learning model, while using the remaining 61 to test a derived model. We extracted 851 features from ultrasound video clips. After dimensionality reduction with the least absolute shrinkage and selection operator (LASSO) regression, 14 features were finally selected and the support vector machine (SVM) and random forest (RF) algorithms were used to establish radiomics models based on those features. In addition, we creatively proposed a machine learning models aided diagnosis algorithm (MLAD) composed of SVM, RF, and radiologists’ diagnosis based on MRI to evaluate the performance of ML models in computer-aided diagnosis (CAD). We evaluated the area under the curve (AUC) as well as the sensitivity, specificity, and precision of the ML models and radiologists’ diagnosis based on MRI by employing receiver operator characteristic curve (ROC) analysis.ResultsThe AUC, sensitivity, specificity, and precision of the SVM in the diagnosis of PCa in the validation set and the test set were 0.78, 63%, 80%; 0.75, 65%, and 67%, respectively. Additionally, the SVM model was found to be superior to senior radiologists’ (SR, more than 10 years of experience) diagnosis based on MRI (AUC, 0.78 vs. 0.75 in the validation set and 0.75 vs. 0.72 in the test set), and the difference was statistically significant (p< 0.05).ConclusionThe prediction model constructed by the ML algorithm has good diagnostic efficiency for prostate cancer. The SVM model’s diagnostic efficiency is superior to that of MRI, as it has a more focused application value. Overall, these prediction models can aid radiologists in making better diagnoses.

The research compares the performance of support vector machine (SVM) and random forest algorithms in identifying songs suitable for relaxation in patients with stress problems. The dataset comprises both Thai and international songs categorized into therapy and non-therapy groups. The results demonstrate that the support vector machine achieves an accuracy of 78%, outperforming the random forest with an accuracy of 72%. Precision and F1-score metrics further emphasize the superiority of the support vector machine in classification. Notably, the support vector machine has recall rates of 50% and 100% for therapy and non-therapy classes, respectively, while the random forest has recall from class therapy of 38% and class non-therapy of 100%. The findings suggest that providing individuals with stress issues the opportunity to listen to stress-reducing music can be a viable approach to reducing the need for psychiatric therapy. The support vector machine is a better algorithm than the random forest for classifying songs for relaxation because it is more accurate, precise, and has more even recall rates.