Range Of Machine Learning Algorithms Research Articles

Diabetes mellitus detection using machine learning techniques

Diabetes Mellitus (DM) is a common disease that is spreading worldwide and affecting millions of people. The use of machine learning techniques for the timely and accurate diagnosis of diabetes is the subject of this study. This research makes use of a dataset that includes measures like insulin resistance metrics and blood glucose levels in addition to clinical and demographic data. The important factor leading to the growth of diabetes mellitus is the lifestyle which includes lack of exercise, anxiety, age factor, family history etc. It is a serious condition, if not cured, can lead to several health problems like heart disease, nerve damage, kidney damage. A range of machine learning algorithms and deep learning techniques such as support vector machines, decision trees, and neural networks, are utilized to examine and simulate the connections present in the data. Techniques for feature extraction and selection are applied to improve the models' performance and highlight important factors that lead to the development of diabetes. The goal of the study is to attain high sensitivity and specificity to guarantee the accurate identification of Type 1 and Type 2 diabetes.

IMPLEMENTATION OF MAPPING-BASED MACHINE LEARNING ALGORITHM AS NON-STRUCTURAL DISASTER MITIGATION TO DETECT LANDSLIDE SUSCEPTIBILITY IN TAKARI DISTRICT

This research is primarily dedicated to providing a comprehensive exposition of the methodology applied in the deployment of a cartographic-based machine learning algorithm designed for the precise identification of areas susceptible to landslides within the geographical confines of the Takari District.This research delves into the application of mapping-based machine learning algorithms in the domain of non-structural disaster mitigation, with a specific emphasis on the detection of landslide susceptibility within the Takari District. A range of machine learning algorithms, including Support Vector Machine, Naive Bayes Classifier, Ordinal Logistic Regression, Random Forest, and Decision Tree, were harnessed to evaluate rainfall data within the context of landslide susceptibility. An evaluation of model performance, anchored in accuracy and Kappa metrics, unveiled that both the Ordinal Logistic Regression and Random Forest models exhibited noteworthy precision, reaching a commendable 74.36%. Nevertheless, a meticulous examination of Kappa values disclosed the ascendancy of the Random Forest model, which achieved a superior Kappa value of 0.5397. As portrayed in the visual representation provided, it becomes manifest that the Random Forest algorithm's prognostications yield 66 instances of cloudy atmospheric conditions, 48 occurrences of light precipitation, and 3 episodes of moderate rainfall. These predictions are influenced by several factors, including average temperature, humidity levels, wind speed, duration of sunlight, and wind direction at maximum speed. Consequently, this comprehensive analysis underscores the Random Forest algorithm as the most efficacious model for landslide susceptibility prediction. Furthermore, the study seamlessly integrated overlay maps, encompassing the Slope Inclination Map of the Takari District, Geological Map of the Takari District, and Soil Type Map of the Takari District, to contribute to the formulation of a definitive map delineating the susceptibility to landslides in the Takari District. Furthermore, further research could conduct spatial validation of the model predictions using additional datasets or remote sensing data to validate the accuracy of the landslide susceptibility map and ensure its applicability across different geographical regions.

Diabetes Prediction Using Machine Learning

Diabetes, characterized by high blood sugar levels over time, poses challenges for accurate prediction due to limited labelled data and data quality issues like outliers and missing values. To address these challenges, our research introduces a robust prediction framework. This framework integrates techniques such as outlier rejection, filling missing values, standardizing data, selecting relevant features, and employing K-fold cross-validation. We utilize a range of machine learning algorithms including k-nearest Neighbour, Decision Trees, Random Forest, AdaBoost, Naive Bayes, XGBoost, and Multilayer Perceptron (MLP). Additionally, we propose a weighted ensembling technique to enhance prediction accuracy. The weights for ensembling are determined based on the performance of each classifier using the Area Under ROC Curve (AUC) metric. We optimize model performance through hyperparameter tuning using grid search. Our experiments are conducted using the Pima Indian Diabetes Dataset under uniform conditions. The ensembling classifier we propose achieves superior performance with a sensitivity of 0.789, specificity of 0.934, false omission rate of 0.092, diagnostic odds ratio of 66.234, and AUC of 0.950. This outperforms existing methods by 2.00% in terms of AUC. Our framework surpasses other approaches discussed in the literature and shows promise for improving diabetes prediction. We have released our source code for diabetes prediction to the public.

Enhancing Coronary Heart Disease Risk Prediction with Machine Learning and Deep Learning

Enhancing Coronary Heart Disease Risk Prediction with Machine Learning and Deep Learning

Machine Learning Algorithms for Predictive Maintenance in Industrial Environments: A Comparative Study

In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases.
 
 While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications

Security Challenges of Vehicular Cloud Computing

In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases. While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications.

Machine Learning Algorithms for Predictive Maintenance in Industrial Environments: A Comparative Study

In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases. While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications.

Security Challenges of Vehicular Cloud Computing

In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases.
 
 While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications.

Prediction of intraoperative red blood cell transfusion in valve replacement surgery: machine learning algorithm development based on non-anemic cohort.

Our study aimed to develop machine learning algorithms capable of predicting red blood cell (RBC) transfusion during valve replacement surgery based on a preoperative dataset of the non-anemic cohort. A total of 423 patients who underwent valvular replacement surgery from January 2015 to December 2020 were enrolled. A comprehensive database that incorporated demographic characteristics, clinical conditions, and results of preoperative biochemistry tests was used for establishing the models. A range of machine learning algorithms were employed, including decision tree, random forest, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), support vector classifier and logistic regression (LR). Subsequently, the area under the receiver operating characteristic curve (AUC), accuracy, recall, precision, and F1 score were used to determine the predictive capability of the algorithms. Furthermore, we utilized SHapley Additive exPlanation (SHAP) values to explain the optimal prediction model. The enrolled patients were randomly divided into training set and testing set according to the 8:2 ratio. There were 16 important features identified by Sequential Backward Selection for model establishment. The top 5 most influential features in the RF importance matrix plot were hematocrit, hemoglobin, ALT, fibrinogen, and ferritin. The optimal prediction model was CatBoost algorithm, exhibiting the highest AUC (0.752, 95% CI: 0.662-0.780), which also got relatively high F1 score (0.695). The CatBoost algorithm also showed superior performance over the LR model with the AUC (0.666, 95% CI: 0.534-0.697). The SHAP summary plot and the SHAP dependence plot were used to visually illustrate the positive or negative effects of the selected features attributed to the CatBoost model. This study established a series of prediction models to enhance risk assessment of intraoperative RBC transfusion during valve replacement in no-anemic patients. The identified important predictors may provide effective preoperative interventions.

Fake Job Post Detection using Machine Learning

The proliferation of online job platforms has given rise to a concerning increase in fraudulent job postings, presenting significant risks to job seekers and undermining the credibility of the job market. This research paper aims to address the pressing issue of fake job post identification by leveraging machine learning techniques. The primary objective is to develop a robust automated tool capable of accurately distinguishing between authentic and deceptive job advertisements. The proposed methodology utilizes a range of machine learning algorithms, incorporating supervised learning techniques and natural language processing methods, to analyze and classify job postings. Through the integration of both single classifiers and ensemble classifiers, the system evaluates and compares results, effectively detecting fraudulent job postings on the web. The study underscores the need for a proactive approach, acknowledging the dynamic tactics employed by scammers. Continuous refinement and adaptation of the machine learning models are emphasized to stay ahead of evolving fraudulent strategies. Ultimately, this research contributes to establishing a more secure online job market, fostering trust among job seekers and mitigating the financial and emotional risks associated with deceptive job postings.

Detecting Fake Social Media Profiles Using the Majority Voting Approach

INTRODUCTION: The rise of social media platforms has brought about a concerning surge in the creation of fraudulent user profiles, with intentions ranging from spreading false information and perpetrating fraud to engaging in cyberbullying. The detection of these deceptive profiles has emerged as a critical imperative to safeguard the trustworthiness and security of online communities.OBJECTIVES: This paper focused on the detection and identification of fake social media profiles.METHODS: This paper introduces an innovative approach for discerning and categorizing counterfeit social media profiles by leveraging the majority voting approach. The proposed methodology integrates a range of machine learning algorithms, including Decision Trees, XGBoost, Random Forest, Extra Trees, Logistic Regression, AdaBoost and K-Nearest Neighbors each tailored to capture distinct facets of user behavior and profile attributes. This amalgamation of diverse methods results in an ensemble of classifiers, which are subsequently subjected to a majority voting mechanism to render a conclusive judgment regarding the legitimacy of a given social media profile.RESULTS: We conducted thorough experiments using a dataset containing both legitimate and fake social media profiles to determine the efficiency of our methodology. Our findings substantiate that the Majority Voting Technique surpasses individual classifiers, attaining an accuracy rate of 99.12%, a precision rate of 99.12%, a recall rate of 99.12%, and an F1-score of 99.12%.CONCLUSION: The results show that the majority vote method is reliable for detecting and recognising fake social media profiles.

A personalized prediction model for urinary tract infections in type 2 diabetes mellitus using machine learning.

Patients with type 2 diabetes mellitus (T2DM) are at higher risk for urinary tract infections (UTIs), which greatly impacts their quality of life. Developing a risk prediction model to identify high-risk patients for UTIs in those with T2DM and assisting clinical decision-making can help reduce the incidence of UTIs in T2DM patients. To construct the predictive model, potential relevant variables were first selected from the reference literature, and then data was extracted from the Hospital Information System (HIS) of the Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital for analysis. The data set was split into a training set and a test set in an 8:2 ratio. To handle the data and establish risk warning models, four imputation methods, four balancing methods, three feature screening methods, and eighteen machine learning algorithms were employed. A 10-fold cross-validation technique was applied to internally validate the training set, while the bootstrap method was used for external validation in the test set. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the models. The contributions of features were interpreted using the SHapley Additive ExPlanation (SHAP) approach. And a web-based prediction platform for UTIs in T2DM was constructed by Flask framework. Finally, 106 variables were identified for analysis from a total of 119 literature sources, and 1340 patients were included in the study. After comprehensive data preprocessing, a total of 48 datasets were generated, and 864 risk warning models were constructed based on various balancing methods, feature selection techniques, and a range of machine learning algorithms. The receiver operating characteristic (ROC) curves were used to assess the performances of these models, and the best model achieved an impressive AUC of 0.9789 upon external validation. Notably, the most critical factors contributing to UTIs in T2DM patients were found to be UTIs-related inflammatory markers, medication use, mainly SGLT2 inhibitors, severity of comorbidities, blood routine indicators, as well as other factors such as length of hospital stay and estimated glomerular filtration rate (eGFR). Furthermore, the SHAP method was utilized to interpret the contribution of each feature to the model. And based on the optimal predictive model a user-friendly prediction platform for UTIs in T2DM was built to assist clinicians in making clinical decisions. The machine learning model-based prediction system developed in this study exhibited favorable predictive ability and promising clinical utility. The web-based prediction platform, combined with the professional judgment of clinicians, can assist to make better clinical decisions.

Machine Learning-Based Classification of Small-Sized Wetlands Using Sentinel-2 Images

<abstract> <p>Wetlands are invaluable ecosystems, offering essential services such as carbon sequestration, water purification, flood control and habitat for countless aquatic species. However, these critical environments are under increasing threat from factors like industrialization and agricultural expansion. In this research, we focused on small-sized wetlands, typically less than 10 acres in size, due to their unique ecological roles and groundwater recharge contributions. To effectively protect and manage these wetlands, precise mapping and monitoring are essential. To achieve this, we exploited the capabilities of Sentinel-2 imagery and employ a range of machine learning algorithms, including Random Forest (RF), Classification and Regression Tree (CART), Gradient Tree Boost (GTB), Naive Bayes (NB), k-nearest neighbors (KNN) and Support Vector Machine (SVM). Our evaluation used variables, such as spectral bands, indices and image texture. We also utilized Google Earth Engine (GEE) for streamlined data processing and visualization. We found that Random Forest (RF) and Gradient Tree Boost (GTB) outperformed other classifiers according to the performance evaluation. The Normalized Difference Water Index (NDWI) came out to be one of the important predictors in mapping wetlands. By exploring the synergistic potential of these algorithms, we aim to address existing gaps and develop an optimized approach for accurate small-sized wetland mapping. Our findings will be useful in understanding the value of small wetlands and their conservation in the face of environmental challenges. They will also lay the framework for future wetland research and practical uses.</p> </abstract>

Brain age gap, dementia risk factors and cognition in middle age

AbstractBackgroundBrain Age Gap (BAG) represents the difference between an individual’s predicted age, derived from machine learning models trained on neuroimaging data, and their chronological age. BAG has been associated with dementia and cognition in old age. Less is known relating BAG to dementia risk‐factors or cognitive performance in middle‐age.MethodCognitively healthy, middle‐aged (40‐59 years) subjects from PREVENT‐Dementia had an array of neuropsychological, neuroimaging and genetic assessments. Brain Ages were predicted from T1‐weighted 3T MRI scans. Cognition was assessed using COGNITO. Multiple linear robust regression models tested hypotheses that: (i) BAG is positively associated with dementia risk‐factors; (ii) increased BAG is associated with worse cognition; (iii) longitudinal BAG change is positively associated with longitudinal cognitive deterioration. A range of machine learning algorithms with nested cross‐validation was used to assess whether BAG and cognition could predict each other.Result552 middle‐aged participants (median age 52.8 years) had all baseline assessments; 68 had amyloid PET data and 138 had longitudinal data. Median BAG was ‐2.0 years with a range from ‐21.2 to 18.4 years (Figure 1). BAG in middle‐age was associated with hypertension (p = 0.007) and alcohol intake (p = 0.008) but not APOE4 carrier status (p = 0.14) or amyloid centiloids (p = 0.53). BAG was not associated with cognitive performance either cross‐sectionally (p = 0.74) or longitudinally (p = 0.30), and there was no added predictive value between BAG and cognition. More education was associated with better cognition (p = 4.7×10−11) but not with BAG (p = 0.32).ConclusionEven in midlife, substantial inter‐individual heterogeneity exists in the extent to which brains have aged. BAG in middle‐age is associated with modifiable dementia risk‐factors, suggesting that lifestyle risk‐factor modification needs to start at, or before, midlife to optimise brain health. BAG in middle‐age was not associated with risk‐factors for Alzheimer’s disease (APOE4, amyloid deposition) or cognitive performance, despite there being a plausible and expected association between more education and better cognitive performance. These results are important for understanding brain‐age in middle‐aged populations who might be the target of future dementia‐preventing therapies.

Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement Learning

Embedding covert streams into a cover channel is a common approach to circumventing Internet censorship, due to censors' inability to examine encrypted information in otherwise permitted protocols (Skype, HTTPS, etc.). However, recent advances in machine learning (ML) enable detecting a range of anti-censorship systems by learning distinct statistical patterns hidden in traffic flows. Therefore, designing obfuscation solutions able to generate traffic that is statistically similar to innocuous network activity, in order to deceive ML-based classifiers at line speed, is difficult. In this paper, we formulate a practical adversarial attack strategy against flow classifiers as a method for circumventing censorship. Specifically, we cast the problem of finding adversarial flows that will be misclassified as a sequence generation task, which we solve with Amoeba, a novel reinforcement learning algorithm that we design. Amoeba works by interacting with censoring classifiers without any knowledge of their model structure, but by crafting packets and observing the classifiers' decisions, in order to guide the sequence generation process. Our experiments using data collected from two popular anti-censorship systems demonstrate that Amoeba can effectively shape adversarial flows that have on average 94% attack success rate against a range of ML algorithms. In addition, we show that these adversarial flows are robust in different network environments and possess transferability across various ML models, meaning that once trained against one, our agent can subvert other censoring classifiers without retraining.

A toolbox of machine learning software to support microbiome analysis

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

Computational modelling for predicting rheological properties of composite modified asphalt binders

The complicated viscoelastic characteristics of asphalt binders make it a challenging task to precisely predict their rheological behavior. This study aims to investigate and compare the suitability of response surface methodology (RSM) and machine learning (ML) modeling approaches in predicting the complex modulus (G*), phase angle (δ), and rutting parameter (G*/sinδ) of Nano Silica (NS) and/or waste denim fiber (WDF) modified asphalt binders before and after short-term aging. To achieve this, an experimental scheme was designed for RSM and ML modeling with three variables including NS contents (0–6%), WDF contents (0–6%), and testing temperature (40–76 °C) as the inputs, and provided the G*, δ and G*/sinδ before and after short-term aging as the outputs. A wide range of ML algorithms was evaluated to determine the optimum ML model that can be used to accurately predict the rheological properties of NS/WDF-modified asphalt binders. RSM analysis results indicated that the G*, δ, and G*/sinδ of NS/WDF composite asphalt are significantly affected by the %NS, %WDF, and test temperatures. The RSM-developed models showed coefficient of determination (R2) values exceeding 0.97 for all responses, indicating adequate agreement between experimental results and models developed by RSM. From ML algorithms optimization and among all evaluated ML models, it was found that Gaussian process regression (GPR) exhibited the highest R2 with a value of (0.99) and the lowest Root Mean Square Error (RMSE) with a value of approximately 1%. The performance evaluation of the GPR model for predicting all responses showed a very small difference between the predicted and experimental results, highlighting the prediction accuracy of the developed ML models.

Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches.

Fetal development is a critical phase in prenatal care, demanding the timely identification of anomalies in ultrasound images to safeguard the well-being of both the unborn child and the mother. Medical imaging has played a pivotal role in detecting fetal abnormalities and malformations. However, despite significant advances in ultrasound technology, the accurate identification of irregularities in prenatal images continues to pose considerable challenges, often necessitating substantial time and expertise from medical professionals. In this review, we go through recent developments in machine learning (ML) methods applied to fetal ultrasound images. Specifically, we focus on a range of ML algorithms employed in the context of fetal ultrasound, encompassing tasks such as image classification, object recognition, and segmentation. We highlight how these innovative approaches can enhance ultrasound-based fetal anomaly detection and provide insights for future research and clinical implementations. Furthermore, we emphasize the need for further research in this domain where future investigations can contribute to more effective ultrasound-based fetal anomaly detection.

A filter-based feature selection approach in multilabel classification

Multi-label classification is a fast-growing field of machine learning. Recent developments have shown several applications, including social media, healthcare, bio-molecular analysis, scene, and music classification associated with the multilabel classification. In classification problems, multiple labels (multilabel or more than one class label) are assigned to an unseen record instead of a single-label class assignment. Feature selection is a preprocessing phase used to identify the most relevant features that could improve the accuracy of the multilabel classifiers. The focus of this study is the feature selection method in multilabel classification. The study used a feature selection filter method involving the Fisher score, analysis of variance test, mutual information, Chi-Square, and ensembles of these statistical methods. An extensive range of machine learning algorithms is applied in the modelling phase of a multilabel classification model that includes binary relevance, classifier chain, label powerset, binary relevance KNN, multi-label twin support vector machine, multi-label KNN. Besides, label space partitioning and majority voting of ensemble methods are used and Random Forest is the base learner. The experiments are carried out over five different multilabel benchmarking datasets. The evaluation of the classification model is measured using accuracy, precision, recall, F1 score, and hamming loss. The study demonstrated that the filter methods (i.e. mutual information) having top weighted to features provided significant outcomes.

Modelling methods of artificial intelligence in medical application

Artificial intelligence is a branch of computer science, an intelligent system that can simulate human thinking, recognize complex situations, acquire learning abilities and knowledge, and solve problems. With the continuous development of information technology, artificial intelligence techniques are increasingly being improved and applied to large-scale genetics research, image detection and classification in medicine. Predictive models for medical data can be built using a wide range of machine learning algorithms: decision trees, multilayer perceptrons, plain Bayes, random forests, and support vector machines, etc., thus processing massive, high-dimensional data and conducting medical research. This paper addresses the specific applications of artificial intelligence in medical practice.