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Assessment of Thoracic Pain Using Machine Learning: A Case Study from Baja California, Mexico

Veronica Rojas-Mendizabal, Cristián Castillo-Olea, Alexandra Gómez-Siono, Clemente Zuñiga
2021 International Journal of Environmental Research and Public Health  
For this, two machine learning techniques were used: Tree classification and cross-validation.  ...  , while for F1 a mean (μ) of 91.2% and a standard deviation (σ) of 6.5640.  ...  For this analysis, we considered a sample of 256 patients, and two machine learning techniques were used: Tree classification and cross-validation.  ... 
doi:10.3390/ijerph18042155 pmid:33672112 fatcat:3zppo2efjzgjtmb663s52mdfyu

Selection of Variables in Logistic Regression Model with Genetic Algorithm for Stroke Prediction

Avijit Kumar Chaudhuri, Prof. Dilip K. Banerjee, Dr. Anirban Das
2021 IARJSET  
of stroke risk, and (ii) discover new risk factors.  ...  Genetic algorithms (GA) can be beneficial and efficient for finding a combination of factors for the fastest diagnosis with the highest accuracies, especially when dealing with a large number of complicated  ...  Classification, often known as predictive analytics, is an important component of AI in machine learning (ML).  ... 
doi:10.17148/iarjset.2021.8817 fatcat:fahnidbie5erzig3a5y6hdpola

Hypertension Prediction using Machine Learning Technique

Youngkeun Choi
2020 International Journal of Advanced Trends in Computer Science and Engineering  
Machine learning technology is used in advanced data analysis and optimization approaches for different kinds of medical problems.  ...  Hypertension is complicated, and every year it causes a lot of many severe illnesses such as stroke and heart disease. This study essentially had two primary goals.  ...  A brief description of the function of the machine learning algorithm is learned from previously diagnosed patient cases [3] .  ... 
doi:10.30534/ijatcse/2020/298942020 fatcat:nz7zvf3bsvhb5iprbafsyig6qy

An artificial neural network approach for predicting hypertension using NHANES data

Fernando López-Martínez, Edward Rolando Núñez-Valdez, Rubén González Crespo, Vicente García-Díaz
2020 Scientific Reports  
in the control and detection of hypertensive patients, which is part of the critical factors for diseases of the heart.  ...  in identifying patients with high risk of developing hypertension.  ...  The points of view expressed are those of the authors and not necessarily those of the NIHR, the NHS, the NHANES or the department of health.  ... 
doi:10.1038/s41598-020-67640-z pmid:32606434 fatcat:sgersodqjferhinz3zg3rsn56a

Classification of Neurodegenerative Disorders Based on Major Risk Factors Employing Machine Learning Techniques

Sandhya Joshi, P. Deepa Shenoy, Vibhudendra Simha G.G., Venugopal K. R, L.M. Patnaik
2010 International Journal of Engineering and Technology  
Different models for the classification of AD, VD and PD using various classification techniques such as Neural Networks (NN) and Machine Learning (ML) methods were also developed.  ...  Similarly, for the classification of Parkinson's disease, the risk factors such as stroke, diabetes, genes and age were the vital factors.  ...  Research and Treatment (ISTAART) for providing useful information  ... 
doi:10.7763/ijet.2010.v2.146 fatcat:bvsqo3p4ingo5hgskv2sktlf74

Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning

Shelda Sajeev, Stephanie Champion, Alline Beleigoli, Derek Chew, Richard L. Reed, Dianna J. Magliano, Jonathan E. Shaw, Roger L. Milne, Sarah Appleton, Tiffany K. Gill, Anthony Maeder
2021 International Journal of Environmental Research and Public Health  
Results suggest a potential for improving CVD risk prediction in the Australian population using machine learning models.  ...  This study assessed the ability of machine learning models to predict CVD mortality risk in the Australian population and compare performance with the well-established Framingham model.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijerph18063187 pmid:33808743 fatcat:j5u3qtb63jhelak54namj7n7qa

Health Data-Driven Machine Learning Algorithms Applied to Risk Indicators Assessment for Chronic Kidney Disease

Yen-Ling Chiu, Mao-Jhen Jhou, Tian-Shyug Lee, Chi-Jie Lu, Ming-Shu Chen
2021 Risk Management and Healthcare Policy  
Based on the results of this study, it is recommended that education as the socioeconomic status should be an important factor for CKD, as high educational level showed a negative and highly significant  ...  methods, and they were also clinically recognized as the major risk factors.  ...  Acknowledgments We thank the Ministry of Science and Technology for supporting this research with ID MOST-108-2221-E-161-003-MY2.  ... 
doi:10.2147/rmhp.s319405 pmid:34737657 pmcid:PMC8558038 fatcat:vk7lbdfb4nb4laqhlhxrxi36z4

Predicting Diabetes Mellitus and Analysing Risk-Factors Correlation

Md. Faruque, Asaduzzaman Asaduzzaman, Syed Hossain, Md. Furhad, Iqbal Sarker
2018 EAI Endorsed Transactions on Pervasive Health and Technology  
It helps a patient to be aware of the risk factors related to diabetes.  ...  In this work, we perform four popular machine learning algorithms, such as Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbour (KNN) and C4.5 Decision Tree (DT), on adult population dataset  ...  Mohammad Maruf Faruqi, RMO, ICU, Medical Centre Chittagong, Bangladesh for providing the real diagnostic data and for their cooperation throughout our work.  ... 
doi:10.4108/eai.13-7-2018.164173 fatcat:5ixdgss7ubewfabjv4zphpljz4

Osteoporosis Risk Prediction for Bone Mineral Density Assessment of Postmenopausal Women Using Machine Learning

Tae Keun Yoo, Sung Kean Kim, Deok Won Kim, Joon Yul Choi, Wan Hyung Lee, Ein Oh, Eun-Cheol Park
2013 Yonsei medical journal  
Purpose: A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density.  ...  Conclusion: Considering various predictors associated with low bone density, the machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.  ...  There were several modifications for data analysis.  ... 
doi:10.3349/ymj.2013.54.6.1321 pmid:24142634 pmcid:PMC3809875 fatcat:4oaksaujv5g7bbucjrxsbeohui

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

Stephen F. Weng, Jenna Reps, Joe Kai, Jonathan M. Garibaldi, Nadeem Qureshi, Bin Liu
2017 PLoS ONE  
Machinelearning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction.  ...  OPEN ACCESS Citation: Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N (2017) Can machine-learning improve cardiovascular risk prediction using routine clinical data?  ...  The views expressed are those of the authors and not necessarily those of the NIHR, the NHS, or the Department of Health.  ... 
doi:10.1371/journal.pone.0174944 pmid:28376093 pmcid:PMC5380334 fatcat:u7tjut5c3zgojdvtl7r5sbadte

Silent brain infarction: a quiet predictor of future stroke

Oh Young Bang
2018 Precision and Future Medicine  
A silent brain infarct (SBI) is defined as imaging or neuropathological evidence of brain infarction without a history of acute neurological dysfunction attributable to the lesion.  ...  The number of patients with SBIs is estimated as several-fold higher than the number with clinical stroke. In addition, SBIs have important clinical implications.  ...  The annual inci- For example, SBIs could be a surrogate marker of the possibility of preventing strokes by control of hypertension, the most widely accepted risk factor for SBI.  ... 
doi:10.23838/pfm.2018.00086 fatcat:hdjdoz55d5gfpoqmryxw3jqaza

Analysis of disease comorbidity patterns in a large-scale China population

Mengfei Guo, Yanan Yu, Tiancai Wen, Xiaoping Zhang, Baoyan Liu, Jin Zhang, Runshun Zhang, Yanning Zhang, Xuezhong Zhou
2019 BMC Medical Genomics  
We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set.  ...  Disease comorbidity is popular and has significant indications for disease progress and management.  ...  In addition, we found that several common diseases, such as, heart failure, cerebral infarction and lung disease, were filtered by the three classification methods as the main risk factors for the targeting  ... 
doi:10.1186/s12920-019-0629-x pmid:31829182 pmcid:PMC6907122 fatcat:6bs4qnmhu5dwzbum5skr5wdzzu

Classification and Prediction on the Effects of Nutritional Intake on Overweight/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4–7th Korea National Health and Nutrition Examination Survey

Hyerim Kim, Dong Hoon Lim, Yoona Kim
2021 International Journal of Environmental Research and Public Health  
model with the most popular machine learning models such as logistic regression and decision tree.  ...  In DNN, binary cross-entropy loss function for binary classification was used with Adam optimizer. For avoiding overfitting, dropout was applied to each hidden layer.  ...  ., 2019 [55] showed the association between nutrients and risk of metabolic syndrome using factor analysis in a Japanese population.  ... 
doi:10.3390/ijerph18115597 pmid:34073854 fatcat:cylesuwke5awjb64h2apnymvpy

The role of frailty index in predicting readmission risk following total joint replacement using light gradient boosting machines

Julie Slezak, Liam Butler, Oguz Akbilgic
2021 Informatics in Medicine Unlocked  
Material & methods: We implemented light gradient boosting machine (LightGBM) as the main machine learning technique on data from American College of Surgeons National Surgical Quality Improvement Program  ...  Future studies should also include socioeconomic and behavioral factors as potential predictors of readmission.  ...  Many researchers have assessed the American Society of Anesthesiologists Classification (ASA) as a predictor of surgical risk [14, 15] .  ... 
doi:10.1016/j.imu.2021.100657 fatcat:ioxtxgkolzgk5mzgudwewg7qge

Assessment of Deep Learning Using Nonimaging Information and Sequential Medical Records to Develop a Prediction Model for Nonmelanoma Skin Cancer

Hsiao-Han Wang, Yu-Hsiang Wang, Chia-Wei Liang, Yu-Chuan Li
2019 JAMA dermatology  
The findings of this study suggest that a risk prediction model may have potential predictive factors for nonmelanoma skin cancer.  ...  To use machine learning to develop a prediction model for incident nonmelanoma skin cancer based on large-scale, multidimensional, nonimaging medical information.  ...  We treated this prediction of NMSC risk as a binary classification problem and built a supervised CNN learning model to solve it.  ... 
doi:10.1001/jamadermatol.2019.2335 pmid:31483437 pmcid:PMC6727683 fatcat:3dtplnauw5ha7aad3mfoxwdenm
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