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检索条件"主题词=Machine Learning Algorithms"

共 25367 条记录，以下是91-100 订阅

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Predicting Coronary Heart Disease Mortality Using machine learning algorithms

Predicting Coronary Heart Disease Mortality Using Machine Le...

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Artificial Intelligence x Multimedia (AIxMM), Conference on

作者： Soha Parto Ali Akbar Safavi Shiva Naghsh Mahsa Keikha Amir Sharafkhaneh School of Electrical and Computer Engineering Shiraz University Shiraz Iran Department of Mechatronic Systems Engineering Simon Fraser University Burnaby Canada Department of Medicine Baylor College of Medicine Houston TX USA

ISBN: (数字)9798350390971

ISBN: (纸本)9798350390988

This study aimed to predict Coronary Heart Disease (CHD) mortality over a 10 to 15-year period using machine learning (ML) techniques. We studied a rather large variety of features associated to CHD mortality from the Sleep Heart Health Study (SHHS) dataset. Using the most important features identified through mutual information, several ML algorithms, including LR, SVM, KNN, ETC, and RF, were trained. A 10-fold cross-validation was employed to ensure accuracy. As a result, the KNN algorithm (k = 18) outperformed the others, achieving an accuracy of 76.05%, an AUC of 79.41%, a sensitivity of 81.08%, a specificity of 71.28%, and a precision of 73.48%. Our proposed approach reasonably well predicts CHD mortality many years in advance, aiming to identify critical factors for CHD mortality prediction, monitor patients, and provide early warnings of the need for prevention and treatment through medical intervention.

关键词： Heart Training machine learning algorithms Accuracy Sensitivity Mortality Nearest neighbor methods Prediction algorithms Mutual information Diseases

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Comparative Performance Analysis of machine learning algorithms for Non-Performing Loan Prediction

Comparative Performance Analysis of Machine Learning Algorit...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Tanmay Chaturvedi Sukanta Halder Uppalapati Sudheer kumar Nilanjan Das SK Bittu Department of Electrical Engineering Sardar Vallabhbhai National Institute of Technology Surat India Department of Electrical Engineering Indian Institute of Technology (ISM) Dhanbad India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

This paper investigates the application of machine learning models for Non-Performing Loan (N. P. L) prediction in the banking sector. The study evaluates the accuracy of diverse models, including traditional algorithms and advanced machine learning approaches such as Random Forest, Gradient Boost, XGBoost, LSTM, Decision Tree, Gaussian Naive Bayes and LightGBM. Training of the models are done on the dataset 'Training Data,’ available on Kaggle under the notebook named “Loan Prediction Based on Customer Behaviour”. Data processing and analysis of the given dataset was done on Kaggle's Jupyter Notebook cloud environment by using Python programming libraries. Through rigorous experimentation, it has been attested that Random Forest classifiers stands out to be best performing model when tested across all the performance benchmarks. This information thereby offers valuable guidance for institutions aiming to strengthen N. P. L prediction mechanisms and overall risk management strategies.

关键词： Training Accuracy machine learning algorithms Computational modeling Predictive models Vectors Bayes methods Decision trees Random forests Long short term memory

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Diabetes Prediction in Healthcare Utilizing machine learning algorithms

Diabetes Prediction in Healthcare Utilizing Machine Learning...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Nrusingha Tripathy Sarbeswara Hota Pranati Satapathy Subrat Kumar Nayak Debasish Swapnesh Kumar Nayak Purnya Prava Nayak Department of Computer Science and Engineering Siksha ‘O' Anusandhan Deemed to be University Bhubaneswar Odisha India Department of Computer Application Siksha ‘O' Anusandhan Deemed to be University Bhubaneswar Odisha India Department of IMCA Utkal University Bhubaneswar Odisha India Department of Computer Science and Engineering Centurion University of Technology and Management Bhubaneswar Odisha India Department of Computer Science and Engineering GIET Baniatangi Bhubaneswar Odisha India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

Diabetes mellitus is among the most dangerous conditions that many people suffer from. Age, obesity, poor food, heredity, high blood pressure, and inactivity are some of the variables that might lead to diabetes mellitus. Though there are additional forms of diabetes, such as gestational diabetes, which appears during pregnancy, the two most common types are type 1 and type 2. In data science, machine learning is a relatively new field of study that looks at how machines pick up knowledge via experience. The goal of this work is to build a system that can more accurately forecast a patient's risk of developing diabetes in their early years by utilizing a combination of research data and machine learning techniques. Interpretable findings using logistic regression (LR) show how various risk variables affect the chance of acquiring diabetes. The most crucial characteristics for diabetes prediction may be automatically discovered by neural networks (NN) from unprocessed input data, eliminating the requirement for human feature selection. While compared to previous models, both the logistic regression and the neural network in this study provide a respectable accuracy score following a 10-fold classification.

关键词： Pregnancy Logistic regression Accuracy machine learning algorithms machine learning Medical services Artificial neural networks Prediction algorithms Diabetes Biomedical monitoring

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The Impact of machine learning algorithms on Credit Card Fraud Detection: A Comparative Study

The Impact of Machine Learning Algorithms on Credit Card Fra...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Mohamed Rusaam S Nijanthan V Muthukumaran N Pratheeshba B Riyas Ahamed M Centre for Computational Imaging and Machine Vision Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Credit card fraud detection is a critical challenge for financial institutions, with billions of dollars lost annually due to fraudulent activities. Traditional rule-based systems struggle to keep up with evolving fraud patterns, often producing high false positive rates. This study presents a comparative analysis of machine learning algorithms for credit card fraud detection, focusing on their accuracy, efficiency, and real-time detection capabilities. Using a highly imbalanced dataset containing 284,807 transactions, we evaluated the performance of Logistic Regression, Decision Trees, Random Forest, Support Vector machines (SVMs), Artificial Neural Networks (ANNs), and Gradient Boosting machines (GBMs). The results demonstrate that ensemble methods, particularly GBMs such as XGBoost, outperform simpler models in terms of accuracy and recall, effectively handling the imbalanced data. ANNs also showed promising results but require significant computational resources. The study highlights the need for a balance between accuracy, processing speed, adaptability, and scalability in real-world fraud detection systems. Future research should explore advanced deep learning models, continuous learning, transfer learning, and explainable AI approaches to enhance the transparency and trust in automated fraud detection systems.

关键词： Support vector machines Adaptation models machine learning algorithms Accuracy Computational modeling Visual analytics Scalability Transfer learning Credit cards Fraud

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Enhancing Lung Cancer Detection by Leveraging machine learning algorithms

Enhancing Lung Cancer Detection by Leveraging Machine Learni...

引用

International Conference on Signal Processing and Communication (ICSC)

作者： Anuradha Rani Choudhury Satya Ranjan Panda Ashis Kumar Mishra Jyotirmayee Routray School of Computer Sciences OUTR Bhubaneswar

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

With concerning incidence rates, lung cancer continues to be a worldwide health emergency that necessitates efficient detection techniques to enhance patient outcomes. In order to stop the progression of the disease, lower mortality, and raise survival rates, early and precise diagnosis is essential. However, because of the non-intuitive correlation between characteristics like lymph node involvement and cancer diagnosis, it can be difficult to detect cancer in its early stages, even for skilled medical professionals. To overcome these obstacles, this work uses machine learning approaches. For the categorization of lung cancer, it uses algorithms such as Support Vector machine, Logistic Regression, Naïve Bayes, k-Nearest Neighbors, and Decision Tree. These algorithms use a variety of features to analyzedata and detect the presence of cancer; preprocessing techniques like data slicing and feature scaling are applied. Performance matrices like accuracy, precision, recall and f1 score were taken into consideration. Support Vector machine outperformed other models by achieving an accuracy of 94% along with precision of 92% and recall being 97%. The outcomes highlight SVM's effectiveness in offering trustworthy diagnostic information. In order to effectively tackle this deadly disease, our study not only exhibits the promise of machine learning in early cancer diagnosis and also lays the groundwork for incorporating such approaches into clinical workflows.

关键词： Support vector machines Logistic regression Accuracy machine learning algorithms Lung cancer machine learning Nearest neighbor methods Feature extraction Decision trees Medical diagnostic imaging

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Loan Prediction Analysis Using Innumerable machine learning algorithms

Loan Prediction Analysis Using Innumerable Machine Learning ...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Nikil Aadithyan A Niranjana M Rockgowtham N K. Poongodi Computer Science and Engineering K S Rangasamy College of Technology Namakkal India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Internet loan fraud has emerged as a significant challenge in the financial industry, causing substantial economic losses and undermining trust in digital lending platforms. Applicants’ information includes income, level of education, employment status, loan amount, and credit histories, among other relevant variable information. Data preparation should start with the essential data pre-processing tasks: imputation of missing values for variables, encoding categorical values, and normalizing of numerical features. The dataset is divided between training and testing subsets when evaluating the model. The XG-Boost classifier is utilized to predict loan approvals, with its performance gauged by metrics like accuracy, precision, recall, and F1 score. Hyperparameter tuning is used to improve model performance. Results suggest that the XG-Boost classifier provides high accuracy for loan approval predictions. Also, the feature importance analysis suggests key factors that have a strong influence on the model's decisions, showing the potential of the algorithm in helping financial institutions make better decisions. This study shows the scalability and effectiveness of XGBoost for practical credit risk prediction tasks.

关键词： Training Measurement Accuracy machine learning algorithms Scalability Predictive models Numerical models Reliability Tuning Testing

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Optimizing Stroke Diagnosis: Application of machine learning algorithms for Early Detection

Optimizing Stroke Diagnosis: Application of Machine Learning...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Chetan Sharma Anooja A Jaydeep Kishore Vivek Bhardwaj upGrad Education Private Limited Bangalore India School of Computer Application JECRC University Jaipur Rajasthan India School of Computer Science and Engineering Manipal University Jaipur Jaipur India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Being a top cause of mortality and serious disability, stroke demands urgent diagnosis and impactful prevention. Current innovations in machine learning (ML) show a great capacity for predicting the likelihood of a stroke. This study reports on applying ML algorithms to improve the timely and early identification of stroke, leading to more effective interventions and lowering morbidity and mortality numbers. This work employs a complete dataset that integrates demographic information, medical history, data on lifestyle, and physical measurements. Engineering techniques of a high level were utilized in pre-processing the data to reduce the assumptions that lay underneath. This research applied five machine learning models for the prompt identification of stroke: Decision Tree, Support Vector machine (SVM), Gradient Boosting, K-Nearest Neighbours (KNN), and XG Boost. The number of datasets used for training was 80%, and 20% went to testing. The accuracy of the Gradient Boosting model stood at 96%, outperforming XG Boost at 92% and Decision Tree at 91%. The model with Support Vector machines (SVM) gave 84% accuracy, but all three models illustrated high precision and recall. These results indicate that machine learning models could significantly augment stroke prediction and enhance clinical decision processes.

关键词： Support vector machines Training Technological innovation machine learning algorithms Accuracy Prevention and mitigation Mortality Boosting Decision trees Testing

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Predicting Diabetes Risk: A Comparative Analysis of machine learning algorithms

Predicting Diabetes Risk: A Comparative Analysis of Machine ...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Hardeo Kumar Thakur Dinesh Prasad Sahu Rajnish Kumar Chaturvedi Govind Kumar Jha Divya Kumari School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of CSE Government Engineering College Munger Bihar India USICT GGSIPU Delhi India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Nowadays, diabetes mellitus is a worldwide health concern that is becoming more common. Therefore, for better early detection techniques (machine learning) is need of hour. Six machine learning algorithms are applied in this article to forecast the likelihood of diabetes in three stages: prediabetes, diabetes, and no diabetes. Three metrics i.e., recall, F1 score and precision are applied to evaluate the algorithms performance. Additionally, we have used the Synthetic Minority Oversampling Technique (SMOTE) for improving class imbalance. The results show that different algorithms and stages of diabetes function differently, which emphasizes the necessity of choosing algorithms according to the demands of certain applications.

关键词： Measurement machine learning algorithms Computational modeling Focusing Predictive models Nearest neighbor methods Prediction algorithms Diabetes Complexity theory Risk management

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Enhancing IoT Security: A Comparative Analysis of machine learning algorithms for Attack Detection

Enhancing IoT Security: A Comparative Analysis of Machine Le...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Muskan Garg Sima Computer Science (FPA) DLCSUPVA Rohtak Haryana India Faculty of Planning and Architecture DLCSUPVA Rohtak Haryana India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The applications of the Internet of things(IoT) have grown at an exponential rate in recent years. However, it also brings up security-related concerns. While the common response techniques like firewalls, antivirus software, spyware, and authentication mechanisms offer security in many domains, viruses and intrusion attacks continue to pose a threat. To solve this issue, our research performs a thorough comparison of the different machine learning techniques, evaluating how well they can detect and predict attacks associated with the Internet of Things. This article uses machine learning (ML) algorithms to thoroughly investigate early identification strategies for IoT attacks. We do this by using the UNSW-NB15 Dataset collected from Kaggle. Three machine learning algorithms—Logistic regression (LR), k-nearest neighbors (k-NN), and Gradient boosting (GB) analyzed with the UNSW-NB15 dataset in this research. In order to evaluate how effectively algorithms identify attacks, many performance metrics are used. These metrics include accuracy, recall, F1 score, ROC curve, and confusion matrix. This analysis helps choose the best machine learning methods for a given IoT security scenario. This comparative analysis provides crucial insights to researchers, practitioners, and industry professionals striving to secure IoT ecosystems against expanding cyber threats, laying the foundation for the growth of IoT attack detection approaches.

关键词： Measurement machine learning algorithms Accuracy machine learning Nearest neighbor methods Boosting Prediction algorithms Security Internet of Things Spyware

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Employing machine learning algorithms for the Prediction of Alzhiemer’s Disease

Employing Machine Learning Algorithms for the Prediction of ...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Raja Rajeswari Sethuraman Harshini S Dhaarshieth K R Subhashini M Department of Computer Applications Dr.Mahalingam College of Engineering and Technology Pollachi Tamil Nadu India Swamy Vivekanandha Medical College Hospital and Research Institute Tiruchengode Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Alzheimer's disease is a neurological condition that progressively worsens over time and impairs thinking, memory, social skills, and behavior. Its onset is most commonly seen in the age groups above 65. The World Health Organization's survey indicates that dementia, including AD, is the sixth largest cause of death globally. For older people, this is the most frequent cause of dementia. The word dementia is used to refer to a wide range of brain disorders that are characterized by behavioural abnormalities, loss of memory and reasoning skills, and difficulty carrying out everyday tasks. In order to lower the death rate, early screening for this condition is essential. machine learning techniques are quite useful in the identification of the relation between risk factors of Alzheimer’s disease and its incidence. This study examines the relationship between several characteristics that lower the risk of Alzheimer's disease using a benchmarking dataset of 2149 Americans. Early detection of AD and associated symptoms is more successful when classifiers such as Random Forest, Support Vector machine, K-Nearest Neighbours, XGBoost, CatBoost, and Logistic Regression are used. Classifier performance and AD predictions are evaluated using metrics like as F1-score, Precision, Accuracy, and Recall. According to this suggested study, the Random Forest and CatBoost algorithm predicts the test data with 95.5% accuracy. This score is better than the other modern and latest algorithms.

关键词： Support vector machines Surveys Logistic regression machine learning algorithms Accuracy Refining Prediction algorithms Classification algorithms Alzheimer's disease Random forests

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