☆ 4.6 Article

A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions

SENSORS (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Computer Science, Artificial Intelligence

Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases

Suyel Namasudra et al.

Summary: This paper proposes a novel neural network model for predicting COVID-19 cases, which can accurately forecast the confirmed, recovered, and death cases. Experimental results show that the model trained with the Levenberg Marquardt algorithm performs the best in predicting COVID-19 data.

NEURAL PROCESSING LETTERS (2023)

添加到收藏夹

Article Nutrition & Dietetics

Development of machine learning prediction models to explore nutrients predictive of cardiovascular disease using Canadian linked population-based data

Jason D. Morgenstern et al.

Summary: Machine learning prediction models were developed to explore the relationship between nutrients and cardiovascular disease (CVD) risk. The study found that supplement use, caffeine, and alcohol were the most important nutrition variables for predicting CVD. The models showed competitive predictive performance and revealed both known and novel associations, suggesting the potential of machine learning in nutritional epidemiology.

APPLIED PHYSIOLOGY NUTRITION AND METABOLISM (2022)

添加到收藏夹

Article Engineering, Biomedical

An enhanced Predictive heterogeneous ensemble model for breast cancer prediction

S. Nanglia et al.

Summary: Breast cancer, common in both men and women, is difficult to detect in early stages and can be costly and complex to treat, leading to high fatality rates. This paper introduces a heterogeneous ensemble machine learning approach for early detection of breast cancer.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

添加到收藏夹

Article Medicine, General & Internal

A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease

Vijendra Singh et al.

Summary: This study aims to detect and predict Chronic Kidney Disease (CKD) early using a deep learning model, which achieved 100% accuracy by selecting key features and applying machine learning models for classification purposes.

DIAGNOSTICS (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Multi-layer perceptron classification method of medical data based on biogeography-based optimization algorithm with probability distributions

Xu-Dong Li et al.

Summary: This paper introduces an improved method for training multi-layer perceptrons based on the biogeography-based optimization algorithm. By incorporating various probability distributions, the accuracy of medical data classification is enhanced, and the effectiveness of the proposed method is demonstrated through simulation experiments.

APPLIED SOFT COMPUTING (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Decision tree and artificial immune systems for stroke prediction in imbalanced data

Laercio Ives Santos et al.

Summary: The study proposed a machine learning approach based on artificial immune systems and genetic programming for cerebral stroke prediction, which was evaluated on a highly imbalanced dataset and increased model interpretability through a simplification operator.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

添加到收藏夹

Article Computer Science, Theory & Methods

Novel binary logistic regression model based on feature transformation of XGBoost for type 2 Diabetes Mellitus prediction in healthcare systems

Yalin Wu et al.

Summary: The rapidly increasing incidence of diabetes, especially Type-II diabetes, has led to complex complications. This study proposes a novel binary logistic regression model with feature transformation for accurately predicting the specific type of diabetes, and it is adaptable to multiple datasets. The results show that the proposed model achieved a high identification rate in diabetes prediction.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2022)

添加到收藏夹

Article Computer Science, Information Systems

Predicting breast cancer biopsy outcomes from BI-RADS findings using random forests with chi-square and MI features

Sheldon Williamson et al.

Summary: Mammography screening is an important method for early breast cancer detection, but may lead to unnecessary biopsies. The Random Forest classifier with feature selection accurately predicted cancer biopsy outcomes, showing its effectiveness in diagnosis.

MULTIMEDIA TOOLS AND APPLICATIONS (2022)

添加到收藏夹

Article Multidisciplinary Sciences

An effective up-sampling approach for breast cancer prediction with imbalanced data: A machine learning model-based comparative analysis

Tuan Tran et al.

Summary: Early detection of breast cancer is crucial for successful treatment. This study proposed an engineered up-sampling method (ENUS) to improve predictive performance of machine learning models. Experiments showed that ENUS significantly improved the accuracy, sensitivity, and F1 score of the models. XGBoost Tree (XGBTree) with ENUS achieved the best performance, and Cell_Shape and Nuclei were identified as the most important attributes in predicting breast cancer. This comprehensive comparison of machine learning methods provides a tool for healthcare practitioners to effectively detect and treat breast cancer.

PLOS ONE (2022)

添加到收藏夹

Article Veterinary Sciences

A comparison of logistic regression and classification tree to assess brucellosis associated risk factors in dairy cattle

Ameer Megahed et al.

Summary: Machine learning approaches were used to determine the seroprevalence of the contagious zoonotic disease, Brucellosis, and to assess the associated risk factors in densely populated cattle areas in Egypt. Logistic regression and Classification and Regression Tree (CART) data-mining analysis were compared, with CART showing higher accuracy in identifying risk factors. The study found that larger herd size, history of abortion, and poor post-calving hygiene were important risk factors for Brucella infection.

PREVENTIVE VETERINARY MEDICINE (2022)

添加到收藏夹

Article Chemistry, Analytical

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Trang-Thi Ho et al.

Summary: The COVID-19 pandemic has led to a global crisis with increasing cases and shortages of test kits and accurate detection models. Developing an automatic algorithm for early detection is crucial. A higher privacy-preserving federated learning system was proposed to detect COVID-19 while protecting sensitive patient data.

SENSORS (2022)

添加到收藏夹

Article Multidisciplinary Sciences

Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction

Shahadat Uddin et al.

Summary: This paper studies different variants of the k-nearest neighbour (KNN) algorithm and compares their performance in disease prediction. By implementing and experimenting on eight datasets, the study found that accuracy values ranged from 64.22% to 83.62%, with Hassanaat KNN showing the highest accuracy. The study also proposes a relative performance index based on accuracy, precision, and recall measures, identifying Hassanaat KNN as the best performing variant.

SCIENTIFIC REPORTS (2022)

添加到收藏夹

Article Environmental Sciences

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Abdur Rasool et al.

Summary: The study proposed four predictive models based on data exploratory techniques to improve the accuracy of breast cancer diagnosis. These models were validated using standard performance metrics and showed excellent performance in the Wisconsin Diagnostic Breast Cancer dataset.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2022)

添加到收藏夹

Article Nutrition & Dietetics

Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach

Chia-Lin Lee et al.

Summary: This study is the first to use machine learning to predict insulin resistance (IR) in patients with chronic kidney disease (CKD). The results showed that the random forest (RF) algorithm had the best area under the ROC curve and the best SHAP value differentiation. This study also included both macronutrients and micronutrients for the first time. We concluded that machine learning algorithms, particularly RF, can help determine risk factors and predict IR in patients with CKD.

NUTRIENTS (2022)

添加到收藏夹

Article Public, Environmental & Occupational Health

Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU

Alyaa Elhazmi et al.

Summary: The study utilized a decision tree algorithm to examine the association between ICU outcomes and a predefined set of variables in adult COVID-19 patients. The decision tree algorithm was found to be useful in predicting 28-day ICU outcomes, providing assistance for critical decision-making by bedside physicians.

JOURNAL OF INFECTION AND PUBLIC HEALTH (2022)

添加到收藏夹

Article Cardiac & Cardiovascular Systems

A Cardiovascular Disease Prediction Model Based on Routine Physical Examination Indicators Using Machine Learning Methods: A Cohort Study

Xin Qian et al.

Summary: This research aimed to establish a cardiovascular disease (CVD) prediction model using machine learning algorithms for the Xinjiang rural population. The study found that age, systolic blood pressure, blood lipid indicators, body mass index, and body adiposity index were important predictors of CVD onset in this population. The prediction model based on L1-LR demonstrated the best prediction performance.

FRONTIERS IN CARDIOVASCULAR MEDICINE (2022)

添加到收藏夹

Article

A web content mining application for detecting relevant pages using Jaccard similarity

Ahmed Adeeb Jalal et al.

International Journal of Electrical and Computer Engineering (2022)

添加到收藏夹

Article Computer Science, Theory & Methods

SVM-SMO-SGD: A hybrid-parallel support vector machine algorithm using sequential minimal optimization with stochastic gradient descent

Gizen Mutlu et al.

Summary: A parallel-hybrid algorithm combining SVM, SMO, and SGD methods has been proposed to optimize the computation of weight costs in the SVM algorithm. Compared with traditional methods, the proposed algorithm is significantly faster and more efficient in terms of time and memory consumption.

PARALLEL COMPUTING (2022)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks

Ahmet Cinar et al.

Summary: The paper introduces a deep learning architecture based on Hybrid Alexnet-SVM for classifying ECG signals of heart patients, showing that this architecture achieves higher accuracy compared to conventional machine learning classifiers.

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING (2021)

添加到收藏夹

Article Multidisciplinary Sciences

A Review of Interpretable ML in Healthcare: Taxonomy, Applications, Challenges, and Future Directions

Talal A. A. Abdullah et al.

Summary: Machine Learning has shown significant impact in healthcare, but the lack of interpretability is a major barrier to its widespread use. Efforts have been made to propose interpretable machine learning models for more convenient application in healthcare settings. This paper aims to survey and explore the application of IML models in healthcare, addressing current limitations, challenges, and future directions.

SYMMETRY-BASEL (2021)

添加到收藏夹

Article Health Care Sciences & Services

Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study

Yao Tong et al.

Summary: The study aimed to evaluate the generalizability of a new machine learning model for forecasting asthma hospital encounters to an academic healthcare system, achieving a high level of accuracy.

JOURNAL OF MEDICAL INTERNET RESEARCH (2021)

添加到收藏夹

Article Computer Science, Information Systems

A comparison of machine learning algorithms for diabetes prediction

Jobeda Jamal Khanam et al.

Summary: The study utilized data mining, machine learning algorithms, and neural network methods to predict diabetes, using the Pima Indian Diabetes dataset. It was found that the logistic regression and support vector machine models performed well in diabetes prediction, and the neural network model with two hidden layers provided 88.6% accuracy.

ICT EXPRESS (2021)

添加到收藏夹

Article Multidisciplinary Sciences

Random forest-based prediction of stroke outcome

Carlos Fernandez-Lozano et al.

Summary: The study focused on utilizing machine learning techniques to predict mortality and morbidity of stroke patients by examining clinical, biochemical, and neuroimaging factors. A predictive model was generated for long-term outcome prediction, specifically for patients with ischemic stroke and intracerebral hemorrhage.

SCIENTIFIC REPORTS (2021)

添加到收藏夹

Article Medicine, General & Internal

Classification of Chronic Kidney Disease in Sonography Using the GLCM and Artificial Neural Network

Dong-Hyun Kim et al.

Summary: Ultrasound is utilized to diagnose kidney diseases, providing information on kidney size and internal echo characteristics to determine the severity of inflammation, aiding in the diagnosis of chronic kidney disease. Parameters extracted from ultrasound images, along with information on kidney size, are processed by an artificial neural network model to accurately classify chronic kidney disease.

DIAGNOSTICS (2021)

添加到收藏夹

Article Neurosciences

Prediction of Motor Function in Stroke Patients Using Machine Learning Algorithm: Development of Practical Models

Jeoung Kun Kim et al.

Summary: This study demonstrated the utility of machine learning algorithms in predicting motor outcomes in stroke patients at 6 months after the event, with the deep neural network (DNN) model performing the best among the three algorithms tested.

JOURNAL OF STROKE & CEREBROVASCULAR DISEASES (2021)

添加到收藏夹

Article Environmental Sciences

Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients

Irfan Ullah Khan et al.

Summary: This study utilized ML algorithms and DL models to predict the mortality rate in COVID-19 cases, with the DL model performing the best with an accuracy of 0.97, demonstrating its significance over the baseline study in the literature.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2021)

添加到收藏夹

Article Medical Informatics

Predicting Semantic Similarity Between Clinical Sentence Pairs Using Transformer Models: Evaluation and Representational Analysis

Mark Ormerod et al.

Summary: The study developed an NLP system to predict similarity scores for clinical text sentence pairs. Analysis of model outputs revealed failures in accurately modeling semantic similarity when dealing with sentence pairs containing medical prescription details, and a tendency to overpredict similarity when there is significant token overlap. Additionally, analysis of token vectors showed divergent strategies between BERT-style models and XLNet in predicting textual similarity.

JMIR MEDICAL INFORMATICS (2021)

添加到收藏夹

Article Neurosciences

Prognostic Factors in Neurorehabilitation of Stroke: A Comparison among Regression, Neural Network, and Cluster Analyses

Marco Iosa et al.

Summary: This study compared the identification of prognostic factors for stroke rehabilitation among regression, neural network, and cluster analysis, finding similar results on a large data sample. All three methods identified older age, severe impairment, unilateral spatial neglect, and total anterior circulation infarcts as important prognostic factors. The accuracy of neural network decreased on the test sample, while binary regression analysis and cluster analysis showed good performance in terms of specificity and sensitivity, respectively.

BRAIN SCIENCES (2021)

添加到收藏夹

Proceedings Paper Computer Science, Interdisciplinary Applications

beHEALTHIER: A Microservices Platform for Analyzing and Exploiting Healthcare Data

Argyro Mavrogiorgou et al.

Summary: The paper discusses the challenges faced in the era of big data, particularly in the healthcare domain, and introduces beHEALTHIER, a platform based on Microservice Architecture designed to efficiently manage and analyze vast amounts of healthcare data for constructing health policies.

2021 IEEE 34TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS) (2021)

添加到收藏夹

Proceedings Paper Computer Science, Information Systems

e-Health Monitoring System with Diet and Fitness Recommendation using Machine Learning

Divya Mogaveera et al.

Summary: The proposed system aims at improving the health of patients suffering from various diseases by recommending healthier diet and exercise plans based on analysis of health parameters and latest reports. It can be beneficial for doctors to recommend personalized diet and exercise plans.

PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT 2021) (2021)

添加到收藏夹

Proceedings Paper Computer Science, Information Systems

Performance Analysis of Chronic Kidney Disease through Machine Learning Approaches

Minhaz Uddin Emon et al.

Summary: Data mining and machine learning are crucial in healthcare, particularly in analyzing and monitoring major health risks such as chronic kidney disease. Machine learning techniques can enhance the accuracy of diagnosing kidney diseases, with the Random Forest model achieving the highest accuracy of 99%.

PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT 2021) (2021)

添加到收藏夹

Article Computer Science, Information Systems

CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection

Olaide Nathaniel Oyelade et al.

Summary: The COVID-19 pandemic has had a significant impact on global socio-economic affairs, prompting ongoing research into relevant vaccines. Scholars are proposing the use of computational intelligence methods to accelerate disease detection, with a focus on natural language processing, knowledge engineering, and deep learning. The study discussed in this paper introduces a CNN-based method with image pre-processing capability to pre-screen suspected COVID-19 cases and confirm detected cases.

IEEE ACCESS (2021)

添加到收藏夹

Article Computer Science, Information Systems

Prediction of Chronic Kidney Disease-A Machine Learning Perspective

Pankaj Chittora et al.

Summary: This study discusses the prediction of Chronic Kidney Disease using machine learning techniques, with LSVM (with L2 penalty) achieving the highest accuracy of 98.86% in synthetic minority over-sampling technique with full features, and a deep neural network achieving the highest accuracy of 99.6% on the same dataset.

IEEE ACCESS (2021)

添加到收藏夹

Article Computer Science, Information Systems

Stroke prediction from electrocardiograms by deep neural network

Yifeng Xie et al.

Summary: Heart abnormalities detected by ECG can serve as diagnostic indicators for brain diseases, and deep learning can establish the correlation between ECG and stroke without expert knowledge. A study using DenseNet model for stroke prediction achieved high accuracy, demonstrating the value of ECG in stroke diagnostics.

MULTIMEDIA TOOLS AND APPLICATIONS (2021)

添加到收藏夹

Article Computer Science, Information Systems

Improving the Prediction of Heart Failure Patients' Survival Using SMOTE and Effective Data Mining Techniques

Abid Ishaq et al.

Summary: The study analyzed heart failure patient survival data, applied multiple classification models for prediction, and ultimately achieved the best accuracy with Extra Tree Classifier in data processing and training stages.

IEEE ACCESS (2021)

添加到收藏夹

Review Computer Science, Information Systems

Applications of Artificial Intelligence and Machine learning in smart cities

Zaib Ullah et al.

COMPUTER COMMUNICATIONS (2020)

添加到收藏夹

Article Environmental Sciences

Comparing Logistic Regression Models with Alternative Machine Learning Methods to Predict the Risk of Drug Intoxication Mortality

YoungJin Choi et al.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2020)

添加到收藏夹

Article Medicine, General & Internal

Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging

Seung Hoon Yoo et al.

FRONTIERS IN MEDICINE (2020)

添加到收藏夹

Article Medicine, General & Internal

Utilization of machine-learning models to accurately predict the risk for critical COVID-19

Dan Assaf et al.

INTERNAL AND EMERGENCY MEDICINE (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier

Warda M. Shaban et al.

KNOWLEDGE-BASED SYSTEMS (2020)

添加到收藏夹

Article Chemistry, Multidisciplinary

AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals

Jaehak Yu et al.

APPLIED SCIENCES-BASEL (2020)

添加到收藏夹

Article Health Care Sciences & Services

Prediction of Type 2 Diabetes Risk and Its Effect Evaluation Based on the XGBoost Model

Liyang Wang et al.

HEALTHCARE (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A plug 'n' play approach for dynamic data acquisition from heterogeneous IoT medical devices of unknown nature

Argyro Mavrogiorgou et al.

EVOLVING SYSTEMS (2020)

添加到收藏夹

Article Medicine, General & Internal

Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study

Bach Xuan Tran et al.

JOURNAL OF CLINICAL MEDICINE (2019)

添加到收藏夹

Proceedings Paper Engineering, Electrical & Electronic

Hybrid Computational Intelligence Technique: Eczema Detection

Yash Kumar Arora et al.

PROCEEDINGS OF THE 2019 IEEE REGION 10 CONFERENCE (TENCON 2019): TECHNOLOGY, KNOWLEDGE, AND SOCIETY (2019)

添加到收藏夹

Review Computer Science, Information Systems

A Review of Machine Learning and IoT in Smart Transportation

Fotios Zantalis et al.

FUTURE INTERNET (2019)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Fetal health status prediction based on maternal clinical history using machine learning techniques

Akhan Akbulut et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2018)

添加到收藏夹

Review Clinical Neurology

Artificial intelligence in healthcare: past, present and future

Fei Jiang et al.

STROKE AND VASCULAR NEUROLOGY (2017)

添加到收藏夹

Article Multidisciplinary Sciences

Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia

Liyan Pan et al.

SCIENTIFIC REPORTS (2017)

添加到收藏夹

Article Computer Science, Artificial Intelligence

CHARACTERIZATION OF SYMBOLIC RULES EMBEDDED IN DEEP DIMLP NETWORKS: A CHALLENGE TO TRANSPARENCY OF DEEP LEARNING

Guido Bologna et al.

JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH (2017)

添加到收藏夹

Proceedings Paper Computer Science, Information Systems

Efficient Heart Disease Prediction System using K-Nearest Neighbor Classification Technique

Nida Khateeb et al.

INTERNATIONAL CONFERENCE ON BIG DATA AND INTERNET OF THINGS (BDIOT 2017) (2017)

添加到收藏夹

Review Cardiac & Cardiovascular Systems

Mortality in heart failure patients

Ibadete Bytyci et al.

ANATOLIAN JOURNAL OF CARDIOLOGY (2015)

添加到收藏夹

Review Biochemistry & Molecular Biology

Machine learning applications in cancer prognosis and prediction

Konstantina Kourou et al.

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2015)

添加到收藏夹

Article Ergonomics

A practical tool for public health surveillance: Semi-automated coding of short injury narratives from large administrative databases using Naive Bayes algorithms

Helen R. Marucci-Wellman et al.

ACCIDENT ANALYSIS AND PREVENTION (2015)

添加到收藏夹

Article Respiratory System

Development of a decision tree to assess the severity and prognosis of stable COPD

C. Esteban et al.

EUROPEAN RESPIRATORY JOURNAL (2011)

添加到收藏夹

Article Computer Science, Information Systems

Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus

Nahla H. Barakat et al.

IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE (2010)

添加到收藏夹

Article Statistics & Probability

Exploratory data analysis

Stephan Morgenthaler

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS (2009)

添加到收藏夹

Review Computer Science, Artificial Intelligence

The use of artificial neural networks in decision support in cancer: A systematic review

Paulo J. Lisboa et al.

NEURAL NETWORKS (2006)

添加到收藏夹

Review Computer Science, Artificial Intelligence

A review of explanation methods for Bayesian networks

C Lacave et al.

KNOWLEDGE ENGINEERING REVIEW (2002)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.