Performance of Machine Learning Algorithms for Credit Risk Prediction with Feature Selection

The liver, a life-sustaining organ, plays a substantial role in many body functions. Liver diseases have become an important world health problem in terms of prevalence, incidences, and mortalities. Liver fibrosis/cirrhosis is great of importance, because if not treated in time liver cancer could be occurred and spread to other parts of the body. For this reason, early diagnosis of liver fibrosis/cirrhosis gives significance. Accordingly, this study investigated the performances of different machine learning algorithms for prediction of liver fibrosis/cirrhosis based on demographic and blood values. In this context, random forest, k nearest neighbour, C4.5 decision tree, K-star, random tree and reduced error pruning tree algorithms were used. Two distinct approaches were employed to evaluate the performances of machine learning algorithms. In the first approach, the entire features of dataset were utilized, while in the second approach, only the features selected through principal component analysis were used. Each approach was rigorously assessed using both 10-fold cross-validation and data splitting (70% train and 30% test) techniques. By conducting separate evaluations for each approach, a comprehensive understanding of the effectiveness of utilizing all features versus extracted features based principal component analysis was attained, providing valuable insights into the impact of feature dimensionality reduction on model performance. In this study, all analyses were implemented on WEKA data mining tool. In the first approach, the classification accuracies of random forest algorithm were 89.72% and 90.75% with the application of data splitting (70%-30%) and cross-validation techniques, respectively. In the second approach, where feature reduction is performed using principal component analysis technique, the accuracy values obtained from data splitting and cross-validation techniques of random forest algorithm were 88.61% and 88.83%, respectively. The obtained results revealed out that random forest algorithm outperformed for both approaches. Besides, the application of principal component analysis technique negatively affected the classification performance of used machine learning algorithms. It is thought that the proposed model will guide specialist physicians in making appropriate treatment decisions for patients with liver fibrosis/cirrhosis, potentially leading to death in its advanced stages.

Performance evaluation of machine learning techniques in predicting cumulative absolute velocity

Alzheimer’s disease is a progressive neurodegenerative disorder for which early detection remains a significant challenge due to the complexity of clinical features and the high dimensionality of medical data. This study aims to improve the accuracy and reliability of Alzheimer’s disease detection by evaluating the performance of multiple machine learning algorithms integrated with intelligent feature selection strategies. Five classification models, Decision Tree, Naïve Bayes, Random Forest, Logistic Regression, and Deep Learning, were investigated under two experimental scenarios: without feature selection and with feature selection using Recursive Feature Elimination, Binary Particle Swarm Optimization, and Variance Threshold. Model performance was evaluated using K-fold cross-validation based on accuracy, precision, recall, and F1-score metrics. The results demonstrate that feature selection consistently enhances classification performance, particularly for conventional machine learning models such as Random Forest and Logistic Regression. Although the Deep Learning model achieves competitive accuracy, its reduced precision and F1-score indicate limitations when applied to reduced feature spaces. These findings highlight the importance of incorporating appropriate feature selection techniques to address data complexity and improve the effectiveness of early Alzheimer’s disease detection.

Pressure gradient (PG) in liquid-liquid flow is one of the key components to design an energy-efficient transportation system for wellbores. This study aims to develop five robust machine learning (ML) algorithms and their fusions for a wide range of flow patterns (FP) regimes. The MLs include Support Vector Machine (SVM), Gaussian Process (GP), Random Forest (RF), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and fusions of these five MLs. A total of eleven hundred experimental data points for nine FPs (two stratified and seven dispersed patterns) in horizontal wellbores are used to develop the MLs. The MLs' performance is evaluated using the metrics including mean absolute percentage error (MAPE), median absolute percentage error (MdAPE), coefficient of variation of root mean squared error (CV-RMSE), and adjusted coefficient of determination. The evaluation metrics are cross-validated using a repeated train-test split strategy. Seven important predictor variables are identified using a supervised feature selection approach: oil and water velocities, FP, input diameter, oil and water density, and oil viscosity. The results show that the high PG prediction accuracy can be achieved using GP compared to other MLs except for the ML-fusions (p < 0.05). A Friedman's test and Wilcoxon Sign-Rank post hoc analysis with Bonferroni correction show that PG prediction errors using GP are significantly lower than using the ANN model (p < 0.05). The values are 18.44 % and 23.9 % for CV-RMSE, 11.6 % and 10.06 % for MAPE, and 7.5 % and 6.75 % for MdAPE, using ANN and GP, respectively. While the previous studies mostly used ANN to demonstrate the capability of MLs to predict PG over the mechanistic or correlation-based models, the present research has shown that GP is even better than ANN using a wide range of FPs and a large data set. • Machine-learning algorithms are developed to predict the pressure gradient. • The Gaussian Process model produces significantly lower prediction error. • The median absolute percentage error using Gaussian Process is 6.75 %. • More than 95 % variation of pressure gradient can be explained using seven variables.

Introduction: Breast cancer is one of the most common cancers among women compared to all other ones. Machine learning techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as a strong technique. Using machine learning models performed on multidimensional dataset, this article aims to find the most efficient and accurate machine learning models for tumor classification prediction.Materials &amp; Methods: Several supervised machine learning algorithms were utilized to diagnosis and prediction of cancer tumor such as Logistic Regression Decision Tree, Random Forest and KNN. The algorithms are applied to a dataset taken from the UCI repository including 699 samples. The dataset includes Breast cancer features. To enhance the algorithms’ performance, these features are analyzed, the feature importance score and cross validation are considered. In this paper ML algorithms improved coupled by limited and selective features to produce high classification accuracy in tumor classification.Results: As a result of evaluation, Logistic Regression algorithm with accuracy value equal to 99.14%, AUC ROC equal to 99.6%, Extra Tree algorithm with accuracy value equal to 99.14% and AUC ROC equal to 99.1% have better performance than other algorithms. therefore, these techniques can be useful for diagnosis and prediction of cancer tumor and prescribe it correctly.Conclusions: The technique of machine learning can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of machine learning to evaluate breast cancer and indeed, the diagnosis and prediction of breast cancer is compared to determine the most appropriate classifier.

Integrating near-infrared hyperspectral imaging with machine learning and feature selection: Detecting adulteration of extra-virgin olive oil with lower-grade olive oils and hazelnut oil

Background: The merging of machine learning and deep learning approaches offers a viable route for improving breast cancer prediction in this era of advanced technology and data-driven healthcare. This study focused on the use of supervised machine learning and deep learning algorithms for breast cancer prediction using Wisconsin breast cancer dataset. Methods: The features from this dataset were computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. Supervised machine learning algorithms, such as logistic regression, random forests, and support vector machines were compared with deep learning algorithms like convolutional and recurrent neural networks (RNN and CNN) for the prediction of breast cancer. The techniques combine clinical, genomic, and image-based data, that both aid in early diagnosis and individualized treatment suggestions. Results: The results of model comparison were very promising, with models obtaining excellent accuracy, sensitivity, and specificity in breast cancer diagnosis. The study used a pair plot and different classifiers with their accuracy scores on test dataset to determine which machine learning model was most suitable based on the relationship of variables represented in the plot. Support vector machine (SVM) has the highest accuracy of 98.8% and Artificial Neural Network (ANN) has lowest accuracy of 95.80% using confusion matrix and the accuracy metric. Conclusion: The study revealed that Support Vector Machine (SVM) has the highest prediction capability for Breast Cancer using Supervised Machine Learning and Deep Leaning. The new classifier performed better than the existing breast cancer prediction models in the literature.

Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2021 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research. Keywords: Research methods in machine learning, machine learning algorithms, machine learning techniques.

SummaryAccurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

P125. Development of a novel ensemble machine learning algorithm for prediction of complications and readmission after anterior cervical spinal fusion

In the context of rapid economic development, the credit risk assessment of small and micro enterprises has become the focus of attention in the financial field. The random forest algorithm is widely used in credit risk assessment due to its high accuracy and robustness, but it has some problems, such as difficulty in parameter selection and performance degradation in processing unbalanced datasets. In this study, we propose a credit risk prediction model for small and micro enterprises based on Particle Swarm Optimization Random Forest Algorithm (PSO-RF). The particle swarm optimization effectively solves the problem of random forest parameter selection and improves the prediction performance of the model through the balance of global search and local search. Experimental results show that the PSO-RF algorithm shows significant performance advantages in credit risk prediction, performs well in dealing with unbalanced datasets, and has certain advantages in feature selection. This study provides new ideas and methods for the credit risk assessment of small and micro enterprises, and is of great significance for improving the risk management capabilities of financial institutions and supporting the healthy development of small and micro enterprises.

Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.

Objective: This study aimed to use machine learning (ML) for the prediction of intraoperative packed red cell (PRC) transfusion and the number of units of transfused PRC, as well as estimate the net benefit of the ML models through decision curve analysis. Methods: The retrospective cohort study was conducted on patients who underwent cranial operations. Clinical data and transfusion data were extracted. Supervised ML algorithms were trained and tested as ML classification for the prediction of intraoperative PRC transfusion and ML regression for predicting the number of transfused PRC units. Results: Out of 2683 patients, 42.9% of neurosurgical patients intraoperatively received PRC. Artificial neural network, gradient boosting classifier, and random forest were the algorithms that had high area under the receiver operating characteristic curve of 0.912, 0.911, and 0.909, respectively, in ML classification, while random forest with regression had the lowest root mean squared error and mean absolute error in ML regression. Conclusions: ML is one of the most effective approaches to developing clinical prediction tools that can enhance the efficiency of blood utilization. Additionally, ML has become a valuable tool in modern health technologies as the computerized clinical decision support systems assist the physician in decision-making in real-world practice.

Aiming at the problem of high‐dimensional features and data imbalance in credit risk assessment that affect model prediction results, a credit risk prediction method based on integrated learning is proposed from three levels of features, data and algorithms. First, a hybrid filter and Random Forest feature selection method is used to select features. This method uses the improved Relief algorithm to initially select features, then combines the maximum information coefficient to eliminate redundant features, and uses the Random Forest algorithm to further reduce the feature dimension. Second, on the basis of the Borderline‐SMOTE method, an adaptive idea is introduced to generate a different number of new samples for each minority sample at the boundary, and a new interpolation method is used to make the new sample distribution more reasonable, so as to reduce the sample imbalance. Finally, the Focal Loss is used to improve the loss function of LightGBM, and the sample weight is adjusted through the parameters α and γ in the Focal Loss function so that the model pays more attention to minority samples and difficult‐to‐classify samples, and improves the accuracy of model classification. And use the improved algorithm as the base classifier and then use AdaBoost and random subspace methods to integrate to establish a credit risk prediction model. Through comparative experiments with other methods, the results show that this method effectively improves the G‐mean value and the AUC value and has a better default prediction effect.