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检索条件"主题词=Machine Learning Algorithms"

共 25808 条记录，以下是651-660 订阅

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Comparative Analysis of machine learning algorithms For Heart Disease Prediction: A Focus On Feature Importance and Model Performance

Comparative Analysis of Machine Learning Algorithms For Hear...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Madhuri Sharma Jaydipkumar Devabhai Pandya Ruchi Thakkar Rakesh Kumar Sharma Arshdeep Chopra Ramavatar Singh Tyagi Dept. of Life Sciences CHRI Campus Gwalior India Dept. of Humanities & S.Science Madhav University Pindwara India Dept. of Biotechnology & L. Sci Mangalayatan University Aligarh India Dept. of Pharmacy Lingayas Vidyapeeth Faridabad India Dept. of Nursing & Paramedical Sciences J.S. University Shikohabad India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Heart disease is one of the top priority health issues in the world, and early detection is paramount to enhancing the efficacy of treatment and reducing complications. Recently, machine learning has shown promises in predicting heart disease. The present study examines predictions based on logistic regression, random forests, and support vector machines on realistic data sets. Strong predictors were found to include age, type of chest pain, maximum heart rate, and exercise-induced angina. Results indicate that machine learning can develop a new wave in heart disease prediction, but also a constant focus on the feature selection and feature engineering steps may play an essential role in bringing up the best accuracy from any model in this research context.

关键词： Support vector machines Heart rate Logistic regression machine learning algorithms Pain Computational modeling Predictive models Vectors Random forests Diseases

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Chronic Kidney Disease Prediction Using machine learning algorithms

Chronic Kidney Disease Prediction Using Machine Learning Alg...

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Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), International Conference on

作者： T. Nagamani R. Kavya D. Lavanya S. Miruthula Department of Computer Science and Engineering Kongu Engineering College Erode Tamil Nadu India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Early detection and characterisation play a crucial role in effectively managing and treating chronic illness. Given the increasing prevalence of chronic kidney disease (CKD), the healthcare system faces a significant burden, as CKD raises the risk of developing end-stage renal disease and related morbidity and mortality. The timely identification of CKD is vital to saving numerous lives. This study uses a feature selection strategy alongside various machine learning (ML) algorithms to develop a diagnostic system for detecting CKD. The study utilised approximately 400 clinical records of CKD patients sourced from the UCI machine learning repository. To refine the datasets for the predictive model, various data preparation techniques were applied, including feature encoding, label encoding, data cleaning, ambiguity removal, handling missing values, and feature selection. Ultimately, the Extra Trees classifier outperformed the other two machine learning models, exhibiting the capability to diagnose chronic kidney disease (CKD) with an impressive 98% accuracy rate and a low 2% true negative rate, all without any data leakage.

关键词： machine learning algorithms Accuracy Ultrasonic imaging Feature extraction Prediction algorithms Chronic kidney disease Encoding

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Heart Attack Prediction Using machine learning algorithms

Heart Attack Prediction Using Machine Learning Algorithms

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Manoj Kumar Dogga Pavan Sekhar Kesanakurthi Naga Siddhartha Urmila Pilania Ketha Sathwik Reddy Alasyam Shashank Computer Science and Engineering Manav Rachna Univeristy Faridabad India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

Heart attack is one of the primary reasons of death worldwide, demanding developments in prognostic healthcare. The research work recognizes the use of 5 machine learning (ML) techniques to detect chances of heart attack. For the work dataset used contain patient data like age, sex, blood pressure, cholesterol levels, and many more medical parameters. Authors detected the chances of heart attack and also compare the performance of these machine learning techniques which are Logistic Regression, Gaussian Naïve Bayes, Bemoulli Naïve Bayes, Support Vector machine, Gradient Boosting to validate the results. Data preprocessing algorithms are utilized for handling missing values, normalization of data, and feature selection for recognition of the most important predictors. The proposed machine learning techniques are assessed on performance metrics accuracy, precision, recall, and F1-score. The experimental results proved the effectiveness of the machine learning algorithms for prediction of heart attack. SVM performed best and Gradient Boosting performed least among the proposed algorithms. The value of F-1-Score calculated to be highest for the SVM which is 92.5%. SVM algorithm achieved maximum precision as per the experimental results which is 93.9%. Highest AUC is attained by SVM algorithm which is 91.8%. SVM algorithm recall is attained as 91.1%. In future authors will focus on integration of more diverse datasets with advanced deep learning techniques to further enhance detection accuracy.

关键词： Support vector machines Measurement machine learning algorithms Accuracy Data preprocessing Cardiac arrest Prediction algorithms Boosting Data models Bayes methods

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Impact of Feature Selection and Interactions on Accuracy of Classical machine learning algorithms: A Driver Behavior Case Study

Impact of Feature Selection and Interactions on Accuracy of ...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Rabab Gamal Mirvat Al-Qutt Heba Khaled Mahmoud Fayez Said Ghoniemy Computer Systems Dept Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

This study aims to investigate how the number of features and their interrelationships impact the accuracy of classical machine learning algorithms, focusing on driver behavior as a case study. As sensor data becomes increasingly available in modern vehicles, there is a growing interest in understanding driver behavior patterns for various applications, including safety and personalized services. However, the specific influence of different features and their interactions on the accuracy of machine learning models in predicting driver behavior remains unclear. To address this gap, the study utilizes a comprehensive dataset called UAH-DriveSet. Multiple subsets of the dataset are created by gradually adding or removing features, allowing for an examination of the impact of feature quantity. Additionally, feature engineering techniques are applied to explore the relationships and interactions between the features. The study employs a set of classical machine learning algorithms, including logistic regression, decision trees, random forests, support vector machines, naive Bayes, k-nearest neighbors, linear discriminant analysis, and AdaBoost, to model driver behavior based on various feature subsets. The performance of each algorithm is evaluated using accuracy and F1-score as the evaluation metric. The findings of the study reveal that the number and selection of features that were extracted after the preprocessing of the data set, play a crucial role in determining the accuracy of machine learning models for predicting driver behavior. The preprocessing is carried out using an autoencoder model for dimension reduction, and Singular value decomposition to get the most effective column to be converted to the alphabet using k-means, then extracting the motifs representing the new feature.

关键词： Support vector machines machine learning algorithms Accuracy Predictive models Feature extraction Road safety Safety

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Early Prediction Model for Anemia in Infants Using Clinical Data from Perú Applying Supervised machine learning algorithms

Early Prediction Model for Anemia in Infants Using Clinical ...

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International Conference on Optimization and Applications (ICOA)

作者： Jonathan Rivera Diego Cardenas José Luis Castillo-Sequera Lenis Wong Universidad Peruana de Ciencias Aplicadas Lima Perú Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798350387353

ISBN: (纸本)9798350387360

Anemia is a disease that is always present from early stages; children are the most prone to contracting it from birth if it's not diagnosed, which can lead to more severe illnesses. Therefore, a predictive model is proposed for the detection of anemia in infants using their clinical data. The Cross Industry Standard Process for Data Mining (CRISP-DM) methodology is employed, along with Analysis Services for the Extract, Transform, Load (ETL) process and four machine learning (ML) algorithms: Logistic Regression (LR), Decision Tree (DT), Support Vector machine (SVM), and Random Forest (RF). All four phases of CRISP-DM were utilized: Business Understanding, Data Understanding, Data Preparation, Modeling, and Evaluation. The dataset used consists of 400,000 records related to the medical history of children from a healthcare institution in Peru, from which 27 variables were selected. The results showed that the DT algorithm performed better, achieving an AUC-ROC Curve greater than 93%, and the “Scikit Learn” library for ML in Python facilitated obtaining the results.

关键词： Support vector machines Pediatrics machine learning algorithms Transforms Predictive models Prediction algorithms Data models Data mining Random forests Standards

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A Novel Fitness Tracker Model Using machine learning algorithms

A Novel Fitness Tracker Model Using Machine Learning Algorit...

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International Conference on Computational Intelligence and Networks (CINE)

作者： Nandini Mishra Ramesh Kumar Thakur School of Computer Engineering KIIT University Bhubaneswar Odisha India Centre of Artificial Intelligence NIT Bhopal Madhya Pradesh India

ISBN: (数字)9798331516796

ISBN: (纸本)9798331516802

In today’s era of fitness tracking, modern devices use metrics like distance covered and heart rate during runs or walks, they fall short when it comes to accurately counting repetitions during weight exercises, particularly with barbells. This project aims to bridge this gap by introducing a cutting-edge machine-learning model tailored specifically for tracking barbell exercises such as Bench Press, Squats, Overhead Presses, Deadlifts, and Rows. This project aims to optimize the accuracy of exercise classification and repetition counting through comprehensive data processing, feature engineering, and predictive modelling. Various machine learning classifiers, including Neural Networks, Random Forests, K-Nearest Neighbors, Decision Trees, and Naive Bayes, are employed for this purpose. Notably, the Decision Tree algorithm achieved the highest accuracy score among the classifiers tested. Thus, leveraging its power, the final model is built upon the Decision Tree algorithm. The ultimate objectives are to enhance the accuracy of exercise identification and accurately count the number of repetitions performed using peak counting algorithms and low-pass filtering techniques applied to accelerometer data. This research contributes to the advancement of personalized fitness tracking, empowering individuals to make informed decisions based on their exercise data.

关键词： Presses machine learning algorithms Accuracy Predictive models Prediction algorithms Classification algorithms Bayes methods Decision trees Object recognition Random forests

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Survey on Predictive algorithms to Detect Insider Threat on a Network Using Different Combination of machine learning algorithms

Survey on Predictive Algorithms to Detect Insider Threat on ...

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Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Favour Femi-Oyewole Victor Osamor Daniel Okunbor Department of Computer and Information Sciences Covenant University Ota Ogun State Nigeria Mathematics & Computer Science Fayetteville State University Fayetteville California

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

This study explores the efficacy of predictive algorithms for insider threat detection in organizational networks. Leveraging machine learning and deep learning techniques, the research identifies state-of-the-art models and assesses challenges associated with scalability and imbalanced datasets. Following the Prisma International Standards methodology, 531 articles were initially retrieved, with 59 high-quality articles selected for detailed analysis. The findings reveal a multifaceted approach, with 58.8% proposing new models, 23.5% implementing and evaluating, and 17.6% lacking explicit metrics. SVM and RNNs emerged as frequently used algorithms, reflecting versatility and effectiveness in network traffic analysis for insider threat detection. The study provides insights into current trends, challenges, and potential avenues for future research in the realm of insider threat detection.

关键词： Measurement Adaptation models machine learning algorithms Telecommunication traffic Predictive models Prediction algorithms Feature extraction

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Predicting Crop Prices using machine learning algorithms for Sustainable Agriculture

Predicting Crop Prices using Machine Learning Algorithms for...

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IEEE Region 10 Symposium

作者： Iftekhar Mahmud Puja Rani Das Md. Habibur Rahman Ahmed Rafi Hasan Kamrul Islam Shahin Dewan Md. Farid Department of Computer Science and Engineering United International University United City Madani Avenue Badda Dhaka Bangladesh Software Engineering The Maersk Mc-Kinney Moller Institute University of Southern Denmark Denmark

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

The agricultural sector is crucial for South Asia's economy, providing employment and driving GDP growth. Accurate price forecasting is essential for food security, market stability, and facilitating import-export activities. In this study, Decision Tree, AdaBoost, and Random Forest have proven effectiveness in predicting crop prices after analyzing different regression models. The Decision Tree has achieved R-squared $(R^{2})$ values of 0.996, 0.996, and 1 and Mean Squared Error MSE values of 0.242, 4.827, and 0.846 respectively for Rice, Onion, and Oil Seed. Similarly, the AdaBoost has also performed well with R-squared $(R^{2})$ values of 0.737, 0.959 and 1 and MSE values of 175.234, 14.566 and 1.643 respectively for Tomato, Potato and Oil Seed, indicating better prediction accuracy and lower prediction error. Lastly, Wheat has performed well with Linear Regression with R-squared $(R^{2})$ values of 0.980 and MSE values of 0.756. Random Forest Regression has shown consistent high R-squared $(R^{2})$ values and low MSE values. The effectiveness of Decision Tree, AdaBoost, and Random Forest in Bangladesh's agricultural price forecasting highlights their versatility, aiding informed decisions by farmers and policymakers.

关键词： Accuracy machine learning algorithms Oils Linear regression Predictive models Stability analysis Seeds (agriculture) Forecasting Random forests Regression tree analysis

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Artificial Intelligence and machine learning algorithms to Analyse Integrated Nutrient Management Strategies for Optimal Yield and Quality of Oilseeds Crops for Sustainable Development

Artificial Intelligence and Machine Learning Algorithms to A...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： Mithra C Showkat A. Dar Preethi A Dept. of Computer Science and Engineering School of Computing (SOC) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. of Computer Science and Engineeing Githam University India Dept. of Computer Science and Engineering Chidambaram India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

The main objective of this research is to deduce the efficacy of integrated nutrient management (INM) technologies in production of oilseed crops for sustainable development. A great amount of experience is needed in selecting the most effective INM strategy. A new recommendation system to circumnavigate this issue is proposed. It lets farmers decide on the best INM strategy to maximize oilseed crop yield and quality. This system is built on the techniques of advanced machine learning (ML) and aritifical Intelligence (AI). Oilseed crop date in Tamil Nadu from 1961 to 2019 was used to develop the proposed algorithm. The proposed algorithm for crop yield prediction (CYP) which includes a Soft Voting Ensemble Classifier with weights (SVECWW), a Soft Voting Ensemble Classifier without weights (SVECWOW) along with the SVM technique are compared and contrasted with existing algorithms and also proves that SVECWW outperforms other ML algorithms with an accuracy rate of 97.2%. Furthermore, the Stacked Generalization Ensemble model is employed and compared with another Deep Neural Network (DNN) for the INM crop recommendation system which offers a simple graphical user interface (GUI) for farmers to use and received an accuracy of 97.5%. This GUI enables farmers to access valuable information such as the optimal timing for cultivating oilseed crops, the appropriate types and quantities of organic manures, inorganic fertilizers, and bio-fertilizers required for successful oilseed crop production. The study shows, on its whole, how to create tailored recommendation systems for farmers using GUI models with artificial intelligence and machine learning algorithms. Implementing these systems is expected to significantly improve oilseed crop production and quality significantly, benefiting the whole agricultural sector for sustainable development. Artificial intelligence (AI) makes a recommendation system more accurate and adaptable by looking through complex datasets and patterns

关键词： machine learning algorithms Accuracy Prediction algorithms Nutrients Classification algorithms Sustainable development Crop yield Recommender systems Fertilizers Graphical user interfaces

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Comparison of machine learning algorithms for Propagation Path Loss Prediction Using Ray Tracing Data

Comparison of Machine Learning Algorithms for Propagation Pa...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Chaewon Yoon Junseok Kim Hyuk-Je Kim Chung Sup Kim Sung Woong Choi Il-Kyoo Lee Dept of electrical electronics& Control Engineering Kongju National University Republic of Korea Radio Research Division ETRI Daejeon Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

In this paper, we compared several regression algorithms through machine learning approach to predict path loss in an outdoor environment using only the location information of Tx and Rx. We constructed an outdoor environment with intersections, buildings and collected path loss data through Ray-tracing. After training by adjusting hyperparameters, two algorithms with low Mean Squared Error (MSE) values were compared in detail by statistically comparing the values of Mean Absolute Error (MAE), MSE, Root Mean Square Error (RMSE), and $\mathbf{R}^{\mathbfalgorithms}$ using K-fold cross validation. The results showed that the K-Nearest Neighbor (K-NN) algorithm had the best prediction performance for path loss. The prediction model also showed faster prediction time compared to the estimation time of Ray tracing.

关键词： Training machine learning algorithms machine learning Ray tracing Predictive models Nearest neighbor methods Prediction algorithms Propagation losses Information and communication technology Root mean square

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