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Towards Analyzing the Prediction of Developing Cardiovascular Disease using Implementation of Machine Learning Techniques

G. Angayarkanni
2020 International Journal for Research in Applied Science and Engineering Technology  
Machine Learning is an emerging technique widely penetrated into the prediction of Cardiovascular Disease (CVD) events.  ...  Cardiovascular Disease is the leading global cause of death among the diabetes and non-diabetes patients.  ...  CONCLUSION AND FUTURE WORK The above study shows the comparative assessment of the different machine learning approaches towards the developing of CVD among the type 2 diabetic and non diabetic patients  ... 
doi:10.22214/ijraset.2020.31268 fatcat:3nddsqzqbrbozdncji6almae3i

Machine Learning and Data Mining Methods in Diabetes Research

Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, Ioanna Chouvarda
2017 Computational and Structural Biotechnology Journal  
The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction  ...  A wide range of machine learning algorithms were employed.  ...  Acknowledgements This work has been partially supported by Horizon 2020 Framework Programme of the European Union under grant agreement 644906, the AEGLE project.  ... 
doi:10.1016/j.csbj.2016.12.005 pmid:28138367 pmcid:PMC5257026 fatcat:gq3lcg5i7jal7ps45vrbje6ufu

Application of Artificial Intelligence in Diabetes Education and Management: Present Status and Promising Prospect

Juan Li, Jin Huang, Lanbo Zheng, Xia Li
2020 Frontiers in Public Health  
Despite the rapid development of science and technology in healthcare, diabetes remains an incurable lifelong illness.  ...  With the information and evidence collected, this review attempts to provide insight and guidance for the development of prospective, data-driven decision support platforms for diabetes management, with  ...  INTRODUCTION Diabetes mellitus (DM) is a long-term chronic disease.  ... 
doi:10.3389/fpubh.2020.00173 pmid:32548087 pmcid:PMC7273319 fatcat:yywyforz7fb6hmwzckf2456t2y

Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning

Sharen Lee, Jiandong Zhou, Wing Tak Wong, Tong Liu, William K K Wu, Ian Chi Kei Wong, Qingpeng Zhang, Gary Tse
2021 BMC Endocrine Disorders  
Higher HbA1c and lipid variability measures were associated with increased risks of neurological, ophthalmological and renal complications, as well as incident dementia, osteoporosis, peripheral vascular  ...  The primary outcome is all-cause mortality. Secondary outcomes were diabetes-related complications.  ...  Diabetic patients who are on insulin are more advanced in the disease life course, and as such are at a higher risk of complications and death.  ... 
doi:10.1186/s12902-021-00751-4 pmid:33947391 pmcid:PMC8097996 fatcat:evuefz6meneubipg5nk2ge2khy

Impact of acute hyperglycemic crisis episode on survival in individuals with diabetic foot ulcer using a machine learning approach

Liling Deng, Puguang Xie, Yan Chen, Shunli Rui, Cheng Yang, Bo Deng, Min Wang, David G. Armstrong, Yu Ma, Wuquan Deng
2022 Frontiers in Endocrinology  
HCE significantly increased the risk of mortality in patients with DFUs (hazard ratio, 1.941; 95% CI 1.018-3.700; P = 0.044) and was independent from other confounding factors (age, sex, diabetes duration  ...  The XGBoost model also revealed that HCE was one of the most important risk factors associated with all-cause mortality in patients with DFUs.ConclusionsDFUs-HCE had significantly lower immediate survival  ...  Acknowledgments The authors thank all of the patients and control subjects for participation in the study.  ... 
doi:10.3389/fendo.2022.974063 pmid:36093085 pmcid:PMC9452661 fatcat:zmkwcy6c5zbxzfy6ywysfucx7m

Identification of risk factors for patients with diabetes: diabetic polyneuropathy case study

Oleg Metsker, Kirill Magoev, Alexey Yakovlev, Stanislav Yanishevskiy, Georgy Kopanitsa, Sergey Kovalchuk, Valeria V Krzhizhanovskaya
2020 BMC Medical Informatics and Decision Making  
Existing studies of diabetes polyneuropathy considered a limited number of predictors in one study to enable a comparison of efficiency of different machine learning methods with different predictors to  ...  The purpose of this study is the implementation of machine learning methods for identifying the risk of diabetes polyneuropathy based on structured electronic medical records collected in databases of  ...  Authors' contributions GK and OM were responsible for a literature review, setting up the concept and methods and writing the manuscript. KM was responsible for the data analysis.  ... 
doi:10.1186/s12911-020-01215-w pmid:32831065 pmcid:PMC7444272 fatcat:vzrujd6tljhrrpjm2jd6jlb4fu

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Ivan Contreras, Josep Vehi
2018 Journal of Medical Internet Research  
Conclusions: We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes  ...  One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.  ...  long-term complications (eg, nephropathy, retinopathy, diabetic foot, cardiovascular disease, or stroke).  ... 
doi:10.2196/10775 pmid:29848472 pmcid:PMC6000484 fatcat:2ncnce6nxzfxtdxu3g5dbt3riq

Usefulness of machine learning in COVID-19 for the detection and prognosis of cardiovascular complications

Allison Zimmerman, Dinesh Kalra
2020 Reviews in cardiovascular medicine  
Artificial intelligence is a broad term that encompasses different tools, including various types of machine learning and deep learning.  ...  Machine learning is a software solution with the ability to analyze large amounts of data and make predictions without prior programming.  ...  Acknowledgments We would like to express my gratitude to all those who helped me during the writing of this manuscript. Conflict of interest The authors declare no conflicts of interest statement.  ... 
doi:10.31083/j.rcm.2020.03.120 pmid:33070540 fatcat:uiwanbws2nc6tg4teqkuccj7vy

Supervised Machine Learning based Ensemble Model for Accurate Prediction of Type 2 Diabetes [article]

Ramya Akula, Ni Nguyen, Ivan Garibay
2019 arXiv   pre-print
of whether a patient has diabetes or not.  ...  Hence, we take a step further and incorporate all the algorithms into a weighted average or soft voting ensemble model where each algorithm will count towards a majority vote towards the decision outcome  ...  It is a high-risk factor of living with diabetes as it could lead to cardiovascular problems. • Height, Weight-BMI: According to National Institute of Diabetes and Digestive and Kidney Diseases 3 , experts  ... 
arXiv:1910.09356v1 fatcat:7hdjnqjtgbarjne6q6qqtnedxa

Toward Machine-Learning-Based Decision Support in Diabetes Care: A Risk Stratification Study on Diabetic Foot Ulcer and Amputation

Zeinab Schäfer, Andreas Mathisen, Katrine Svendsen, Susanne Engberg, Trine Rolighed Thomsen, Klaus Kirketerp-Møller
2021 Frontiers in Medicine  
We further use machine learning techniques to assess the practical usefulness of such risk factors for predicting foot ulcers and amputation.  ...  We analyze the data of 246,705 patients with diabetes to assess some of the main risk factors for developing DFU/amputation.  ...  The second aim is to utilize machine learning (ML) approaches to predict the occurrence of DFU and amputation in order to assess the practical usefulness of risk factors based on general socio-economic  ... 
doi:10.3389/fmed.2020.601602 pmid:33681236 pmcid:PMC7931152 fatcat:le7iec7cnffhppfquruapj7cwq

A Machine Learning Predictive Model for Post-Ureteroscopy Urosepsis Needing Intensive Care Unit Admission: A Case–Control YAU Endourology Study from Nine European Centres

Amelia Pietropaolo, Robert M. Geraghty, Rajan Veeratterapillay, Alistair Rogers, Panagiotis Kallidonis, Luca Villa, Luca Boeri, Emanuele Montanari, Gokhan Atis, Esteban Emiliani, Tarik Emre Sener, Feras Al Al Jaafari (+4 others)
2021 Journal of Clinical Medicine  
The aims of our paper were to analyse the predictors for severe urosepsis using a machine learning model (ML) in patients that needed intensive care unit (ICU) admission and to make comparisons with a  ...  With the rise in the use of ureteroscopy and laser stone lithotripsy (URSL), a proportionate increase in the risk of post-procedural urosepsis has also been observed.  ...  Acknowledgments: We would like to thank all of the hospitals and surgeons for contributing data. Conflicts of Interest: No conflict of interest. J. Clin. Med. 2021, 10, 3888  ... 
doi:10.3390/jcm10173888 pmid:34501335 fatcat:zxtpfhy47fdezkglqe3zc33zpi

Predicting the development of type 2 diabetes in a large Australian cohort using machine learning techniques (Preprint)

Lei Zhang, Xianwen Shang, Subhashaan Sreedharan, Xixi Yan, Jianbin Liu, Stuart Keel, Jinrong Wu, Wei Peng, Mingguang He
2019 JMIR Medical Informatics  
A one-time self-reported survey can accurately predict the risk of diabetes using a machine-learning approach. Achieving a healthy BMI can significantly reduce the risk of developing T2DM.  ...  We aimed to develop a substantially improved diabetes risk prediction model using sophisticated machine-learning algorithms based on a large retrospective population cohort of over 230,000 people who were  ...  Toward this end, in this study, we present a machine learning-based diabetes risk prediction tool using only self-reported information.  ... 
doi:10.2196/16850 pmid:32720912 fatcat:6g6rxkhyjzaathyx6yqcnfd35u

A Systematic Literature Review on Obesity: Understanding the Causes & Consequences of Obesity and Reviewing Various Machine Learning Approaches Used to Predict Obesity

Mahmood Safaei, Elankovan A. Sundararajan, Maha Driss, Wadii Boulila, Azrulhizam Shapi'i
2021 Computers in Biology and Medicine  
Therefore, the machine learning approach is a promising solution to early predictions of obesity and the risk of overweight because it can offer quick, immediate, and accurate identification of risk factors  ...  Overweight and obesity are one of the main lifestyle illnesses that leads to further health concerns and contributes to numerous chronic diseases, including cancers, diabetes, metabolic syndrome, and cardiovascular  ...  Understanding the causes and determinants of obesity is a critical step toward creating effective policy and developing workable prevention programs due to the aforementioned additional complications.  ... 
doi:10.1016/j.compbiomed.2021.104754 pmid:34426171 fatcat:rrqxcur3b5b6pmcjqlc6hele4y

A machine learning approach to predict early outcomes after pituitary adenoma surgery

Todd C. Hollon, Adish Parikh, Balaji Pandian, Jamaal Tarpeh, Daniel A. Orringer, Ariel L. Barkan, Erin L. McKean, Stephen E. Sullivan
2018 Neurosurgical Focus  
can be predicted with 87% accuracy using a machine learning approach.  ...  Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can  ...  and long-term outcomes.  ... 
doi:10.3171/2018.8.focus18268 fatcat:6emwdwqdb5grtpfbkh33cebpqe

Patient clusters based on HbA1c trajectories: A step toward individualized medicine in type 2 diabetes

Tomas Karpati, Maya Leventer-Roberts, Becca Feldman, Chandra Cohen-Stavi, Itamar Raz, Ran Balicer, Tatsuo Shimosawa
2018 PLoS ONE  
Conclusions By applying unsupervised machine learning to longitudinal HbA1c trajectories, we have identified clusters of patients who have distinct risk for diabetes-related complications.  ...  In the clinical relevance assessment, HbA1c levels demonstrated a J-shape association with the risk for outcomes.  ...  Acknowledgments The authors thank Carly Davis-Pask, MPH, of the Clalit Research Institute for her editorial work on this manuscript.  ... 
doi:10.1371/journal.pone.0207096 fatcat:ltfbgf4flnhifiddnhdvmwqlbq
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