Effectiveness in health care is a specific characteristic of each intervention and outcome evaluated. Especially with regard to surgical interventions, organization, structure and processes play a key role in determining this parameter. In addition, health care services by definition operate in a context of limited resources, so rationalization of service organization becomes the primary goal for health care management. This aspect becomes even more relevant for those surgical services for which there are high volumes. Therefore, in order to support and optimize the management of patients undergoing surgical procedures, the data analysis could play a significant role. To this end, in this study used different classification algorithms for characterizing the process of patients undergoing surgery for a femoral neck fracture. The models showed significant accuracy with values of 81%, and parameters such as Anaemia and Gender proved to be determined risk factors for the patient\'s length of stay. The predictive power of the implemented model is assessed and discussed in view of its capability to support the management and optimisation of the hospitalisation process for femoral neck fracture, and is compared with different model in order to identify the most promising algorithms. In the end, the support of artificial intelligence algorithms laying the basis for building more accurate decision-support tools for healthcare practitioners.

BACKGROUND: The study aimed to determine the most crucial parameters associated with CVD and employ a novel data ensemble refinement procedure to uncover the optimal pattern of these parameters that can result in a high prediction accuracy.
RESULTS: Data were collected from 369 patients in total, 281 patients with CVD or at risk of developing it, compared to 88 otherwise healthy individuals. Within the group of 281 CVD or at-risk patients, 53 were diagnosed with coronary artery disease (CAD), 16 with end-stage renal disease, 47 newly diagnosed with diabetes mellitus 2 and 92 with chronic inflammatory disorders (21 rheumatoid arthritis, 41 psoriasis, 30 angiitis). The data were analyzed using an artificial intelligence-based algorithm with the primary objective of identifying the optimal pattern of parameters that define CVD. The study highlights the effectiveness of a six-parameter combination in discerning the likelihood of cardiovascular disease using DERGA and Extra Trees algorithms. These parameters, ranked in order of importance, include Platelet-derived Microvesicles (PMV), hypertension, age, smoking, dyslipidemia, and Body Mass Index (BMI). Endothelial and erythrocyte MVs, along with diabetes were the least important predictors. In addition, the highest prediction accuracy achieved is 98.64%. Notably, using PMVs alone yields a 91.32% accuracy, while the optimal model employing all ten parameters, yields a prediction accuracy of 0.9783 (97.83%).
CONCLUSIONS: Our research showcases the efficacy of DERGA, an innovative data ensemble refinement greedy algorithm. DERGA accelerates the assessment of an individual\'s risk of developing CVD, allowing for early diagnosis, significantly reduces the number of required lab tests and optimizes resource utilization. Additionally, it assists in identifying the optimal parameters critical for assessing CVD susceptibility, thereby enhancing our understanding of the underlying mechanisms.

UNASSIGNED: The emergence and rapid spread of multidrug-resistant bacteria (MRB) caused by the excessive use of antibiotics and the development of biofilms have been a growing threat to global public health. Nanoparticles as substitutes for antibiotics were proven to possess substantial abilities for tackling MRB infections via new antimicrobial mechanisms. Particularly, carbon dots (CDs) with unique (bio)physicochemical characteristics have been receiving considerable attention in combating MRB by damaging the bacterial wall, binding to DNA or enzymes, inducing hyperthermia locally, or forming reactive oxygen species.
UNASSIGNED: Herein, how the physicochemical features of various CDs affect their antimicrobial capacity is investigated with the assistance of machine learning (ML) tools.
UNASSIGNED: The synthetic conditions and intrinsic properties of CDs from 121 samples are initially gathered to form the raw dataset, with Minimum inhibitory concentration (MIC) being the output. Four classification algorithms (KNN, SVM, RF, and XGBoost) are trained and validated with the input data. It is found that the ensemble learning methods turn out to be the best on our data. Also, ε-poly(L-lysine) CDs (PL-CDs) were developed to validate the practical application ability of the well-trained ML models in a laboratory with two ensemble models managing the prediction.
UNASSIGNED: Thus, our results demonstrate that ML-based high-throughput theoretical calculation could be used to predict and decode the relationship between CD properties and the anti-bacterial effect, accelerating the development of high-performance nanoparticles and potential clinical translation.

In recent times, various machine learning approaches have been widely employed for effective diagnosis and prediction of diseases like cancer, thyroid, Covid-19, etc. Likewise, Alzheimer\'s (AD) is also one progressive malady that destroys memory and cognitive function over time. Unfortunately, there are no dedicated AI-based solutions for diagnoses of AD to go hand in hand with medical diagnosis, even though multiple factors contribute to the diagnosis, making AI a very viable supplementary diagnostic solution. This paper reports an endeavor to apply various machine learning algorithms like SGD, k-Nearest Neighbors, Logistic Regression, Decision tree, Random Forest, AdaBoost, Neural Network, SVM, and Naïve Bayes on the dataset of affected victims to diagnose Alzheimer\'s disease. Longitudinal collections of subjects from OASIS dataset have been used for prediction. Moreover, some feature selection and dimension reduction methods like Information Gain, Information Gain Ratio, Gini index, Chi-Squared, and PCA are applied to rank different factors and identify the optimum number of factors from the dataset for disease diagnosis. Furthermore, performance is evaluated of each classifier in terms of ROC-AUC, accuracy, F1 score, recall, and precision as well as included comparative analysis between algorithms. Our study suggests that approximately 90% classification accuracy is observed under top-rated four features CDR, SES, nWBV, and EDUC.

It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA) based on 15 easily accessible hematological indices. A database of 1596 patients with COVID-19 was used; it was divided into 1257 training datasets (80 % of the database) for training the algorithms and 339 testing datasets (20 % of the database) to check the reliability of the algorithms. The optimal combination of hematological indicators that gives the best prediction consists of only four hematological indicators as follows: neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, ferritin, and albumin. The best prediction corresponds to a particularly high accuracy of 97.12 %. In conclusion, our novel approach provides a robust model based only on basic hematological parameters for predicting the risk for ICU admission and optimize COVID-19 patient management in the clinical practice.

Complement inhibition has shown promise in various disorders, including COVID-19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement-related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID-19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence-based algorithm to predict disease outcome (ICU vs. non-ICU admission). A recently introduced alpha-index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha-index in ranking a substantial number of genetic variants. This approach enables the implementation of well-established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.

In this study, it is aimed to compare the performances of the algorithms by predicting the movement directions of stock market indexes in developed countries by employing machine learning algorithms (MLMs) and determining the best estimation algorithm. For this purpose, the movement directions of indexes such as the NYSE 100 (the USA), NIKKEI 225 (Japan), FTSE 100 (the UK), CAC 40 (France), DAX 30 (Germany), FTSE MIB (Italy), and TSX (Canada) were estimated by employing the decision tree, random forest k-nearest neighbor, naive Bayes, logistic regression, support vector machines and artificial neural network algorithms. According to the results obtained, artificial neural networks were found to be the best algorithm for NYSE 100, FTSE 100, DAX 30 and FTSE MIB indices, while logistic regression was determined to be the best algorithm for the NIKKEI 225, CAC 40, and TSX indices. The artificial neural networks, which exhibited the highest average prediction performance, have been determined as the best prediction algorithm for the stock market indices of developed countries. It was also noted that artificial neural networks, logistic regression, and support vector machines algorithms were capable of predicting the directional movements of all indices with an accuracy rate of over 70 %.

We discuss the implementation challenges of gas sensing systems based on low-frequency noise measurements on chemoresistive sensors. Resistance fluctuations in various gas sensing materials, in a frequency range typically up to a few kHz, can enhance gas sensing by considering its intensity and the slope of power spectral density. The issues of low-frequency noise measurements in resistive gas sensors, specifically in two-dimensional materials exhibiting gas-sensing properties, are considered. We present measurement setups and noise-processing methods for gas detection. The chemoresistive sensors show various DC resistances requiring different flicker noise measurement approaches. Separate noise measurement setups are used for resistances up to a few hundred kΩ and for resistances with much higher values. Noise measurements in highly resistive materials (e.g., MoS2, WS2, and ZrS3) are prone to external interferences but can be modulated using temperature or light irradiation for enhanced sensing. Therefore, such materials are of considerable interest for gas sensing.

BACKGROUND: No-show to medical appointments has significant adverse effects on healthcare systems and their clients. Using machine learning to predict no-shows allows managers to implement strategies such as overbooking and reminders targeting patients most likely to miss appointments, optimizing the use of resources.
METHODS: In this study, we proposed a detailed analytical framework for predicting no-shows while addressing imbalanced datasets. The framework includes a novel use of z-fold cross-validation performed twice during the modeling process to improve model robustness and generalization. We also introduce Symbolic Regression (SR) as a classification algorithm and Instance Hardness Threshold (IHT) as a resampling technique and compared their performance with that of other classification algorithms, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), and resampling techniques, such as Random under Sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE) and NearMiss-1. We validated the framework using two attendance datasets from Brazilian hospitals with no-show rates of 6.65% and 19.03%.
RESULTS: From the academic perspective, our study is the first to propose using SR and IHT to predict the no-show of patients. Our findings indicate that SR and IHT presented superior performances compared to other techniques, particularly IHT, which excelled when combined with all classification algorithms and led to low variability in performance metrics results. Our results also outperformed sensitivity outcomes reported in the literature, with values above 0.94 for both datasets.
CONCLUSIONS: This is the first study to use SR and IHT methods to predict patient no-shows and the first to propose performing z-fold cross-validation twice. Our study highlights the importance of avoiding relying on few validation runs for imbalanced datasets as it may lead to biased results and inadequate analysis of the generalization and stability of the models obtained during the training stage.

It has been widely argued among researchers that the application of big data analytics promises to reduce human bias and provide a scientific and evidence-based approach to the judicial process. In this dataset, historical data consisting of appeal cases presented at the Supreme Court of Nigeria (SCN) were collected from an online repository (Primsol Law Pavillion). A total of 5585 appeal cases brought before the SCN were collected from the archive. The dataset consisted of both criminal and civil appeal cases brought before the SCN. Variables that are related to court case proceedings were identified from related literature, verified by legal experts and used as a basis for generating an electronic structured version of the dataset stored as a spreadsheet file from the unstructured data. From the collected data, thirteen input variables were identified with one output/decision variable. The distribution of the numerical variables was presented as a descriptive statistical summary in terms of the minimum, maximum, mode, mean and standard deviation. The developed dataset can assist researchers to build predictive systems by training their models. Various feature extraction techniques can also be applied on the dataset to remove irrelevant or redundant features for increased performance of such classifiers that are needed to predict the outcome of legal cases.