Logistic Regression Algorithm Research Articles

Task allocation algorithm for distributed large data stream group computing in the era of digital intelligence

In the era of digital intelligence, this paper studies the task allocation algorithm of distributed large data stream group computing, and reasonably allocates the task of group computing to meet the needs of massive computing and analysis of distributed large data stream. According to the idea of swarm intelligence perception and crowdsourcing platform, the task allocation model of distributed large data stream group computing is constructed to realize the task allocation of group computing. A distributed large data stream group computing task model and a user model are constructed, user attributes are initialized by using the accuracy of the answers submitted by users, the possibility that users can participate in the group computing task is predicted by a logistic regression algorithm, so that user candidate sequences participating in the computing task can be obtained, and the accuracy of the user’s real topics and corresponding topics can be grasped by capturing the candidate users’ real topics and evaluating the accuracy algorithm. Select the users who meet the subject area, update the candidate user sequence, and filter the users again on the basis of fully considering the factors such as information gain, user integrity and cost, so as to get the final user sequence and complete the task allocation of group computing. Experiments show that this method can solve the problem of distributed large data flow group computing task allocation, achieve high accuracy, reduce the cost, and effectively improve the information gain.

Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making.

The histopathological classification of melanocytic tumours with spitzoid features remains a challenging task. We confront the complexities involved in the histological classification of these tumours by proposing machine learning (ML) algorithms that objectively categorise the most relevant features in order of importance. The data set comprises 122 tumours (39 benign, 44 atypical and 39 malignant) from four different countries. BRAF and NRAS mutation status was evaluated in 51. Analysis of variance score was performed to rank 22 clinicopathological variables. The Gaussian naive Bayes algorithm achieved in distinguishing Spitz naevus from malignant spitzoid tumours with an accuracy of 0.95 and kappa score of 0.87, utilising the 12 most important variables. For benign versus non-benign Spitz tumours, the test reached a kappa score of 0.88 using the 13 highest-scored features. Furthermore, for the atypical Spitz tumours (AST) versus Spitz melanoma comparison, the logistic regression algorithm achieved a kappa value of 0.66 and an accuracy rate of 0.85. When the three categories were compared most AST were classified as melanoma, because of the similarities on histological features between the two groups. Our results show promise in supporting the histological classification of these tumours in clinical practice, and provide valuable insight into the use of ML to improve the accuracy and objectivity of this process while minimising interobserver variability. These proposed algorithms represent a potential solution to the lack of a clear threshold for the Spitz/spitzoid tumour classification, and its high accuracy supports its usefulness as a helpful tool to improve diagnostic decision-making.

Sentiment Analysis - An optimized Weighted Horizontal Ensemble approach

Sentiment Analysis has gained authority as one of the primary means of analyzing feedbacks and opinion by individuals, organizations and governments. The result of sentiment analysis informs an organization on areas to improve and how best to manage customers. While sentiment analysis may be misleading as no algorithm has been considered 100% efficient, the choice of algorithms can optimize the result based on the dataset in question. This paper aims at studying various algorithms and implementing a weighted horizontal ensemble algorithm as a panacea to low confidence level in the results of sentiment analysis. We designed a system that implements the original Naive Bayes algorithm, Multinomial Naïve Bayes algorithm, Bernoulli Native Bayes algorithm, Logistic Regression algorithm, Linear Support Vector Classifier algorithm and the Stochastic Gradient Descent algorithm. Our dataset was sourced from the Stanford University. It contains fifty thousand (50,000) movie reviews. Dataset from the Nigerian movie review was used to test the models. The reviews were encoded as a sequence of word indices. An accuracy of over 91% was achieved. The Ensemble technique delivered an F1-measure of 90%. Ensemble technique provides a more reliable confidence level on sentiment analysis. The researchers also discovered that change in writing style can affect the performance of sentiment analysis

Privacy-preserving multi-party logistic regression in cloud computing

In recent years, machine learning techniques have been widely deployed in various fields. However, machine learning faces problems like high computation overhead, low training accuracy, and poor security due to data silos, privacy issues and communication limitations, especially in the environment of cloud computing. Logistic regression (LR) is a popular machine learning method used for prediction, while current LR algorithms suffer from high computation cost and communication burden due to interactions between users and cloud servers. In this paper, we propose a Privacy-Preserving Multi-party Logistic Regression (PPMLR) algorithm, which achieves privacy-preserving and non-interactive gradient descent regression training in machine learning. PPMLR uses the Distributed two Trapdoors Public-Key Cryptosystem (DT-PKC) as a main building block, which satisfies additive homomorphic encryption. Specifically, users go off-line after encrypting local private data, then the service provider (SP) trains the global logistic regression model by interacting with the cloud server (CS), so that the confidentiality and privacy of user’s private data can be guaranteed during the training process. We prove by detailed security proof that PPMLR guarantees data and model privacy. Finally, we conduct experiments on two popular medical datasets from the UCI machine learning repository. The experimental results show that PPMLR can conduct privacy-preserving training efficiently. Comparison with the stat-of-the-art Privacy-Preserving Logistic Regression Algorithm (PPLRA) shows that the model training time is reduced by about 4 times.

SOIL HEALTH ANALYSIS AND CLASSIFICATION OF MICRONUTRIENTS FOR CROP SUGGESTION USING MACHINE LEARNING

An essential component of agriculture is soil. Many types of soil exists. Different parameters can be observed in each type of soil, and various crops can be grown on different types of soils. To understand which crops develop in certain types of soil, they need to be informed on the characteristics and features of various soil types. The capability to identify the inputs needed for productive and cost-effective farming was provided by soil analysis. A proper soil test will ensure that the proper amount of fertilizers are utilized, providing the crop to meet its needs while also profiting from the nutrients already available in the soil. The phrase "soil health" refers to the biological, chemical, and physical elements of the soil that are significant for plant nutrients. Soil analysis is a collection of different chemical processes that determines the amounts of available plant nutrients in the soil. The most important factors for producing a healthy crop are micronutrient analysis. The classification of soil's micronutrients is the primary objective of this analysis. In this case, machine learning methods may be helpful. It has made significant progress in recent years. Hence in this analysis, soil health analysis and classification of Micronutrients for crop suggestion using Machine Learning is presented. The coloured images of the soil samples are obtained and processed using a number of algorithms and filters. Color, texture, and other features can be extracted using these developed algorithms. Logistic Regression algorithm is used to classify micronutrients.

ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots.

Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providing essential guidance for protein engineering. Aiming at protein-DNA interaction hotspots, this work introduces an effective prediction method, ESPDHot based on a stacked ensemble machine learning framework. Here, the interface residue whose mutation leads to a binding free energy change (ΔΔG) exceeding 2 kcal/mol is defined as a hotspot. To tackle the imbalanced data set issue, the adaptive synthetic sampling (ADASYN), an oversampling technique, is adopted to synthetically generate new minority samples, thereby rectifying data imbalance. As for molecular characteristics, besides traditional features, we introduce three new characteristic types including residue interface preference proposed by us, residue fluctuation dynamics characteristics, and coevolutionary features. Combining the Boruta method with our previously developed Random Grouping strategy, we obtained an optimal set of features. Finally, a stacking classifier is constructed to output prediction results, which integrates three classical predictors, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) as the first layer, and Logistic Regression (LR) algorithm as the second one. Notably, ESPDHot outperforms the current state-of-the-art predictors, achieving superior performance on the independent test data set, with F1, MCC, and AUC reaching 0.571, 0.516, and 0.870, respectively.

Big Data Visualisation of Heart Disease Prediction Using High Level Ada Boosting Algorithm and Gradient Boosting Algorithm

Machine learning and artificial intelligence have been found useful in various disciplines during the course of their development, especially in the enormous increasing data in recent years. It can be more reliable for making better and faster decisions for disease predictions. So, machine learning algorithms are increasingly finding their application to predict various diseases. Constructing a model can also help us visualize and analyze diseases to improve reporting consistency and accuracy. This article has investigated how to detect heart disease by applying various machine learning algorithms. The study in this article has shown a two-step process. The heart disease dataset is first prepared into a required format for running through machine learning algorithms. Medical records and other information about patients are gathered from the UCI repository. The heart disease dataset is then used to determine whether or not the patients have heart disease. Secondly, Many valuable results are shown in this article. The accuracy rate of the machine learning algorithms, such as Logistic Regression, Support vector machine, K-Nearest-Neighbours, Adaboost, and Gradient Boosting Classifier, are validated through the confusion matrix. Current findings suggest that the Logistic Regression algorithm gives a high accuracy rate of 95% compared to other algorithms. It also shows high accuracy for f 1-score, recall, and precision than the other four different algorithms. However, increasing the accuracy rates to approximately 97% to 100% of the machine learning algorithms is the future study and challenging part of this research.

SDN-based detection and mitigation of DDoS attacks on smart homes

The adoption of the Internet of Things (IoT) has proliferated across various domains, where everyday objects like refrigerators and washing machines are now equipped with sensors and connected to the internet. Undeniably, the security of such devices, which were not primarily designed for internet connectivity, is of utmost importance but has been largely neglected. In this paper, we propose a framework for the real-time DDoS attack detection and mitigation in SDN-enabled smart home networks. We capture network traffic during regular operations and during DDoS attacks. This captured traffic is used to train several machine learning (ML) models, including Support Vector Machine (SVM), Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN) algorithms. These trained models are executed as SDN controller applications and subsequently employed for real-time attack detection. While we utilize ML techniques to protect IoT devices, we propose the use of SNORT, a signature-based detection technique, to secure the SDN controller itself. Real-world experiments demonstrate that without SNORT, the SDN controller goes offline shortly after an attack, resulting in a 100% packet loss. Furthermore, we show that ML algorithms can efficiently classify traffic into benign and attack traffic, with the Decision Tree algorithm outperforming others with an accuracy of 99%.

Classification-driven intelligent system for automated evaluation of higher education exam paper quality

AbstractEvaluating the quality of university exam papers is crucial for universities seeking institutional and program accreditation. Currently, exam papers are assessed manually, a process that can be tedious, lengthy, and in some cases, inconsistent. This is often due to the focus on assessing only the formal specifications of exam papers. This study develops an intelligent system for the automatic evaluation of university exam papers in terms of form and content, ensuring adherence to quality standards. The system is composed of two subsystems: the first evaluates compliance with formal specifications, and the second analyzes the content. The content analysis involves automatically categorizing exam questions based on Bloom's cognitive levels (BCLs) and determining the representation ratio of these levels in the exam paper. This subsystem comprises four main modules: 1) question collection, 2) text pre-processing using natural language processing (NLP) methods, 3) feature engineering using the CountVectorizer method to convert questions into feature vectors, and 4) a classification module based on the Logistic Regression (LR) algorithm to categorize exam questions into categories like knowledge, comprehension, application, analysis, synthesis, and evaluation. Experimental results indicate that the system achieves an average accuracy of 98.5%.

Communicative intention detection in Spanish tweets using Jakobson language functions

Social media platforms, such as Twitter (now X), are a major source of communication. Identifying communicative intentions is useful, as it encapsulates the latent motivations that drive text creation. This intention is also helpful in understanding the message, context, and audience. This study proposes a method for detecting communicative intentions in tweets using Jakobson’s language functions. We constructed a meticulously annotated dataset, drawing from the extensive RepLab2013 corpus. Our dataset underwent rigorous scrutiny by linguistic annotators who analyzed over 12,000 tweets individually. These experts identified the dominant language function within each tweet by employing diverse strategies to ensure precise labeling quality. The outcome demonstrated a noteworthy Kappa agreement score of 0.6, reflecting a strong inter-annotator reliability. Subsequently, these functions were mapped to the corresponding intention categories. We employed logistic regression and support vector machines (SVM) algorithms to classify intention in tweets and explored various pre-processing techniques, incorporating n-grams and bag-of-words representations. Furthermore, we expanded our research using pre-trained large language models, incorporating the latest state-of-the-art techniques in natural language processing.

759 Revisiting the ETV Success Score Using Machine Learning Can We Do Better?

INTRODUCTION: The endoscopic third ventriculostomy (ETV) success score (ETVSS) is a useful decision-making heuristic when considering the probability of surgical success, defined as no repeat CSF diversion surgery needed within six months. Nonetheless, the original dataset published in 2009 was relatively small (n = 618) and logistic regression (LR) model performance modest. METHODS: We queried the MarketScan national database for the years 2005-2020 to identify patients <18 years of age who underwent a first-time ETV and subsequently had at least 6 months of continuous enrollment in the database. We collected data on predictors matching the original ETVSS—age, etiology of hydrocephalus, and history of any previous shunt placement. Next, we used six ML algorithms—LR, support vector classifier, random forest, k-nearest neighbors, XGBoost, and Naive Bayes—to develop predictive models. Finally, we used nested cross-validation to assess models’ comparative performances on unseen data. RESULTS: We identified 1698 patients that met inclusion criteria, and 1139 (67%) had successful ETVs. Performance of all models exceeded that of the original ETVSS, with the LR model still performing best with area under the receiver operating characteristic curve (AUROC) 0.90 (95% confidence interval, 0.86-0.94), sensitivity 0.79 (0.70-0.88), and specificity 0.91 (0.87-0.95) at Youden’s index. The random forest was the second-best ML algorithm following closely behind. This compares favorably to the original ETVSS with AUROC 0.80 (0.76-0.84, p < 0.01), sensitivity 0.65 (0.47-0.85, p < 0.01), and specificity 0.71 (0.65-0.75, p < 0.01). CONCLUSIONS: Our predictions for ETV success outperform the current gold standard. Logistic regression may still be the most interpretable and predictive algorithm; further work is underway to externally validate these models.

Strengthening Wireless Network Security: Supervised Machine Learning-Based Intrusion Detection for Enhanced Threat Mitigation

With the wireless networks deemed vital to our laced and unetched world, it has become fundamental to safeguard these networks against the various cyber threats that are on the rise. As a research proposal I show concern about the application of a Supervised Machine Learning-Based Intrusion Detection system, making use of Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Logistic Regression (LR) algorithms. The survey begins with the introduction of an intrusive inter-dataset technology that merges two catalytic datasets into a single efficient model that can address both model security and data sensitivity concerns. The evaluation of several machine learning algorithms within the proposed framework is a core component of the analysis. The accuracy measure as well as others such as recall, precision, and F1 score, are applied to evaluate the algorithms' level in detecting intrusions from different datasets.The investigation shows that the intra-dataset routing noticeably improves the detecting modules' efficiency. Notably, the model of MLP algorithm has enhanced recall which in turn shows the enhancement of the positive instance identification. SVM shows increased precision, which accounts for the improved accuracy since correct names of positive cases are produced more often. LR finds it overall enhance the precisions thus attesting to its efficiency in correct deductions. The research documents the multifaceted nature of IDS, prompting the intervention of the machine learning algorithms with ddoswdelcome intrusion detection system. A study which provides a practical guide for network administrators and security professionals hinting on how to select algorithms to meet the distinct security needs in their enterprises. These findings contribute to the open discourse on wireless network security and serve as a base for future study of this engaging research topic in constantly developing field of intrusion detection.

Prediction of Short-Term Postoperative Complications Following Open Reduction Internal Fixation of Acute Ankle Fractures

Introduction/Purpose: Acute ankle fractures are a common injury and represent a significant financial burden to the healthcare system. Reimbursement following ankle fractures is complicated by bundled payments within the 90-day global period which do not account for unpredictable complications. Comorbidity indices, such as the American Society of Anesthesiologists (ASA) classification, are often correlated with complications; however, the positive predictive value of these metrics have not been investigated. As insurers continue to transition towards bundled reimbursements, greater attentiveness towards providing efficient care is paramount. The purpose of this investigation is to develop a machine learning (ML) model that is predictive of complications following operative management of acute ankle fractures. This study will also compare ML algorithms to legacy indices to assess the predictive value in predicting complications. Methods: The American College of Surgeons-National Surgical Quality Improvement Program database was queried via Current Procedural Terminology (CPT) from 2015-2018 for open reduction internal fixation (ORIF) cases for ankle fractures, including ORIF of the medial malleolus, posterior malleolus, lateral malleolus, bimalleolar, trimalleolar, syndesmosis. Training and validation sets were created by randomly assigning 80% and 20% of the data set. Included variables were age, body mass index (BMI), operative time, smoking status, comorbidities, diagnosis, and preoperative hematocrit and albumin. Complications included any adverse event, transfusion, surgical site infection, return to the operating room, deep vein thrombosis or pulmonary embolism, pneumonia, urinary tract infection, cerebrovascular accident, cardiac arrest, myocardial infarction. Each ML algorithm was compared with one another and to a baseline model using American Society of Anesthesiologists (ASA) classification. Model strength was evaluated by calculating the area under the curve (AUC) for receiver operating characteristic (ROC) of any adverse event. Results: We identified a total of 28,736 ORIF cases. Mean age and BMI were 32.2 ± 18.6 years, 30.8 ± 7.6 kg/m^2, respectively. The overall incidence of any medical complication was 3.4% (n=975) with surgical site infection (1.3%, n=386) and return to operating room (1.3%, n=367) being the most common. Percentage hematocrit, BMI, operative time, and age were of highest importance in outcome prediction. Logistic regression (AUC: 72%) and gradient boosting (AUC: 72%) ML algorithms outperformed ASA classification (AUC: 69%) for predicting any adverse event following ORIF of ankle fractures. Conclusion: Legacy comorbidity indices are simple metrics that can be easily constructed from patient demographic variables and past medical history. However, the predictive power is limited as they have shown a low positive predictive value for predicting complications. Machine learning algorithms can calculate patient-specific risk for postoperative complications which may adjust perioperative care and site of surgery. These models have the capability to optimize utilization of healthcare resources and minimize excessive expenditure by providing patient-specific care. Receiver Operating Curve-Area under the Curve Analysis for Adverse Events Following ORIF of Ankle Fractures

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators.

Obstructive sleep apnea (OSA) is a common and potentially fatal sleep disorder. The purpose of this study was to construct an objective and easy-to-promote model based on common clinical biochemical indicators and demographic data for OSA screening. The study collected the clinical data of patients who were referred to the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University from December 1, 2020, to July 31, 2023, including data for demographics, polysomnography (PSG), and 30 biochemical indicators. Univariate and multivariate analyses were performed to compare the differences between groups, and the Boruta method was used to analyze the importance of the predictors. We selected and compared 10 predictors using 4 machine learning algorithms which were "Gaussian Naive Bayes (GNB)", "Support Vector Machine (SVM)", "K Neighbors Classifier (KNN)", and "Logistic Regression (LR)". Finally, the optimal algorithm was selected to construct the final prediction model. Among all the predictors of OSA, body mass index (BMI) showed the best predictive efficacy with an area under the receiver operating characteristic curve (AUC) = 0.699; among the predictors of biochemical indicators, triglyceride-glucose (TyG) index represented the best predictive performance (AUC = 0.656). The LR algorithm outperformed the 4 established machine learning (ML) algorithms, with an AUC (F1 score) of 0.794 (0.841), 0.777 (0.827), and 0.732 (0.788) in the training, validation, and testing cohorts, respectively. We have constructed an efficient OSA screening tool. The introduction of biochemical indicators in ML-based prediction models can provide a reference for clinicians in determining whether patients with suspected OSA need PSG.

The development of a model for the threat detection system with the use of machine learning and neural network methods

This study examines the development of a model for the threat detection system with the use of machine learning and neural network methods. The fast development of Internet technologies has led to the appearance of many digital systems and platforms. However, despite the impressive technological progress, another side also emerged in the spread of a massive number of different cyber threats. Although various ways have been created to detect and prevent them, the threats are also developing and becoming more complex each year. Therefore, new system defense and data protection methods using machine and deep learning approaches have been proposed recently. The methods based on these approaches have proved to be especially effective in the wave of new Artificial Intelligence applications. In this paper, a threat detection system has been designed to disclose different kinds of threats while maintaining the security, confidentiality, and availability of the computer system. The development of machine learning models for detecting DDoS and man-in-the-middle attacks, Structured Query Language (SQL) injections, phishing, and malware was examined. The data scaling, feature selection, feature extraction, and classification steps were also thoroughly described. Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, XGBoost, CatBoost, and Deep Neural Network algorithms were utilized for training the cyber threat detection models. The experimental results evaluated all the models using accuracy, precision, recall, and F1-score metrics. The best models achieved scores in the range of 0.90 to 1.00.

A Provably Accurate Randomized Sampling Algorithm for Logistic Regression

In statistics and machine learning, logistic regression is a widely-used supervised learning technique primarily employed for binary classification tasks. When the number of observations greatly exceeds the number of predictor variables, we present a simple, randomized sampling-based algorithm for logistic regression problem that guarantees high-quality approximations to both the estimated probabilities and the overall discrepancy of the model. Our analysis builds upon two simple structural conditions that boil down to randomized matrix multiplication, a fundamental and well-understood primitive of randomized numerical linear algebra. We analyze the properties of estimated probabilities of logistic regression when leverage scores are used to sample observations, and prove that accurate approximations can be achieved with a sample whose size is much smaller than the total number of observations. To further validate our theoretical findings, we conduct comprehensive empirical evaluations. Overall, our work sheds light on the potential of using randomized sampling approaches to efficiently approximate the estimated probabilities in logistic regression, offering a practical and computationally efficient solution for large-scale datasets.

Research on improving accuracy and efficiency of animal data collection and classification using machine learning

With the continuous expansion of machine learning algorithms in various application domains, the application value of new algorithms, such as Support Vector Machines and Convolutional Neural Networks in data classification, has garnered increasing attention. This paper takes machine learning algorithms as the research entry point, explores the concept of machine learning, and delves into its application value in data classification. This paper, starting with an overview of machine learning algorithms, analyzes the supervised and unsupervised learning problems in machine learning, focusing on the applications of Convolutional Neural Networks, Support Vector Machine models, and logistic regression algorithms in data classification. This study emphasizes designing and implementing a machine learning-based image classification system. Through an in-depth exploration of the application of machine learning algorithms in data classification, a fully functional system is constructed, encompassing multiple modules, including machine vision and software development. This system accurately classifies and recognizes images, providing practical tools and technical support for image processing and analysis. In this study, the goal of achieving good image classification is realized through research and the application of machine learning algorithms. By designing and implementing a machine learning-based image classification system, the accuracy and efficiency of classification in handling massive data are improved. This system also demonstrates wide-ranging prospects in software development and machine vision, among other fields.

Heart Disease Prediction Using GridSearchCV and Random Forest

INTRODUCTION: This study explores machine learning algorithms (SVM, Adaboost, Logistic Regression, Naive Bayes, and Random Forest) for heart disease prediction, utilizing comprehensive cardiovascular and clinical data. Our research enables early detection, aiding timely interventions and preventive measures. Hyperparameter tuning via GridSearchCV enhances model accuracy, reducing heart disease's burdens. Methodology includes preprocessing, feature engineering, model training, and cross-validation. Results favor Random Forest for heart disease prediction, promising clinical applications. This work advances predictive healthcare analytics, highlighting machine learning's pivotal role. Our findings have implications for healthcare and policy, advocating efficient predictive models for early heart disease management. Advanced analytics can save lives, cut costs, and elevate care quality.
 OBJECTIVES: Evaluate the models to enable early detection, timely interventions, and preventive measures.
 METHODS: Utilize GridSearchCV for hyperparameter tuning to enhance model accuracy. Employ preprocessing, feature engineering, model training, and cross-validation methodologies. Evaluate the performance of SVM, Adaboost, Logistic Regression, Naive Bayes, and Random Forest algorithms.
 RESULTS: The study reveals Random Forest as the favored algorithm for heart disease prediction, showing promise for clinical applications. Advanced analytics and hyperparameter tuning contribute to improved model accuracy, reducing the burden of heart disease.
 CONCLUSION: The research underscores machine learning's pivotal role in predictive healthcare analytics, advocating efficient models for early heart disease management.

Machine Learning May Be An Alternative To BIPSS In The Differential Diagnosis Of ACTH-Dependent Cushing's Syndrome.

This study aimed to develop machine learning (ML) algorithms for the differential diagnosis of adrenocorticotropic hormone (ACTH)-dependent Cushing's syndrome (CS) based on biochemical and radiological features. Logistic regression algorithms were used for ML, and the area under the receiver operating characteristics curve (AUROC) was used to measure performance. We used Shapley Contributed Comments (SHAP) values, which help explain the results of the ML models to identify the meaning of each feature and facilitate interpretation. A total of 106 patients, 80 with Cushing's disease (CD) and 26 with ectopic ACTH syndrome (EAS), were enrolled in the study. The ML task was created to classify patients with ACTH-dependent CS into CD and EAS. The average AUROC value obtained in the cross-validation of the logistic regression model created for the classification task was 0.850. The diagnostic accuracy of the algorithm was 86%. The SHAP values indicated that the most important determinants for the model were the 2-day 2-mg dexamethasone suppression test, the > 50% suppression in the 8-mg high-dose dexamethasone test, late-night salivary cortisol, and the diameter of the pituitary adenoma. We have also made our algorithm available to all clinicians via a user-friendly interface. ML algorithms have the potential to serve as an alternative decision support tool to invasive procedures in the differential diagnosis of ACTH-dependent CS.

Early prediction models and crucial factor extraction for first-year undergraduate student dropouts

PurposeThis study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.Design/methodology/approachThe real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.FindingsDT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”Practical implicationsThis work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.Originality/valueAlthough first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.