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Soft computing techniques for early diabetes prediction
2022
Indonesian Journal of Electrical Engineering and Computer Science
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Diabetes mellitus is a chronic, life-threatening, and complicated condition. ...
The techniques reveal promising performance in predicting diabetes reliably and effectively in terms of several classification evaluation metrics, according to experimental analysis and assessment conducted ...
Abdullateef Al-Bayati from Al-Mustansiriyah University, College of Medicine for providing the ground truth judgment of the comparison matrix used in this study. We also would like to thank Dr. ...
doi:10.11591/ijeecs.v25.i2.pp1167-1176
fatcat:op7krxb6yjb4hkbatn3d4o46ru
Medical Big Data Analytics using Machine Learning Algorithms
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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practitioners in better decision-making process. ...
Machine Learning Algorithms (MLA) is a powerful tool which enables computers to learn from data. ...
[37] predicted diabetes mellitus using ensemble Machine learning approach. ...
doi:10.35940/ijitee.a5290.119119
fatcat:wbfxzifl5relpbp7lqom3p63c4
Classifier Algorithms and Ensemble Models for Diabetes Mellitus Prediction: A Review
2021
International Journal of Advanced Trends in Computer Science and Engineering
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Presently, there is a great and wide work carried out on Machine Learning with a focus on medical and its application. ...
Classifiers algorithms such as Naive Bayes, Decision Tree, Artificial Neural Network, Support Vector Machine, K-Nearest Neighbour and Multi-Layer Perceptron. ...
Also in the area of making use of various machine learning algorithms and data mining technology to construct various prediction and analysis models. ...
doi:10.30534/ijatcse/2021/641012021
fatcat:awqfnht2avbydaap7zcyfhcbgu
Machine learning and deep learning predictive models for type 2 diabetes: a systematic review
2021
Diabetology & Metabolic Syndrome
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Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. ...
First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. ...
Acknowledgements We would like to thank Vicerrectoría de Investigación y Posgrado, the Research Group of Product Innovation, and the Cyber Learning and Data Science Laboratory, and the School of Engineering ...
doi:10.1186/s13098-021-00767-9
pmid:34930452
pmcid:PMC8686642
fatcat:ybn34nsodja5lcetnidihomdvy
A multi-class classification model for supporting the diagnosis of type II diabetes mellitus
2020
PeerJ
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Numerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. ...
Machine learning algorithms including instance-based, decision trees, deep neural network, and ensemble algorithms were all used to build the predictive models utilized in this study. ...
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ...
doi:10.7717/peerj.9920
pmid:32974105
pmcid:PMC7487151
fatcat:4vkxeh2hjzgrndnjat6ltilj5y
A Multilayer Hybrid Machine Learning Model for Diabetes Detection
2020
ITM Web of Conferences
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The basic diabetes detection model uses Bayesian classification machine learning algorithm, but even though the model is able to detect diabetes, the efficiency is not acceptable at all times because of ...
A Hybrid Machine Learning Model is used to overcome the drawbacks produced by a single algorithm model. ...
Last but not the least we would also like to thank all those who have directly or indirectly helped us in completion of this thesis. ...
doi:10.1051/itmconf/20203203032
fatcat:kdh77qat7rcm5g4x7z4czrmqgm
Metabolic Syndrome and Development of Diabetes Mellitus: Predictive Modeling Based on Machine Learning Techniques
2019
IEEE Access
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prediction of future onset of diabetes using relevant risk factors of MetS; and 3) to investigate the relative performance of machine learning methods when data sampling techniques are used to generate ...
We also proposed J48 decision tree and Naïve Bayes methods for prediction of future onset of diabetes using relevant risk factors obtained from logistic regression analysis, over balanced and unbalanced ...
They also thank our colleagues at CPCSSN for their help with the datasets. No financial support was received for this study/work. ...
doi:10.1109/access.2018.2884249
fatcat:jigzu53655hrfjucf6emxjlsfe
Machine Learning and Smart Devices for Diabetes Management: Systematic Review
2022
Sensors
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In these studies, wearable devices were used in combination with artificial intelligence (AI) techniques; (4) Conclusions: Wearable devices have attracted a great deal of scientific interest in the field ...
These technologies have been introduced in order to make life easier for patients with diabetes by allowing better control of the stability of blood sugar levels and anticipating the occurrence of dangerous ...
Predictive analytics, when applied to health data, can help make critical choices and predictions. Machine learning and regression techniques are used to perform this predictive analysis. ...
doi:10.3390/s22051843
pmid:35270989
pmcid:PMC8915068
fatcat:2yzmy4dsf5bqtjjgjy4sy7fuqy
Prediction of Patient Readmission via Machine Learning Algorithms
2020
International journal of recent technology and engineering
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Specifically, we explore the different techniques used in a medical area under the machine learning research field. ...
Based on the outcomes of this research, it was found out that (bagging and DT) is the best technique to predict diabetes, whereas SVM is the best technique when it comes to prediction the breast cancer ...
Machine learning techniques were used in healthcare for prediction for example prediction of diseases. ...
doi:10.35940/ijrte.f7770.038620
fatcat:6a6bwuywirceviiebn65zgox7a
Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey
2019
Tehnički Vjesnik
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With the development of Data mining, researchers find that machine learning is playing an increasingly important role in diabetes research. ...
In this paper, conventional machine learning techniques are described in early screening and diagnosis of diabetes, moreover deep learning techniques which have a significance of biomedical effect are ...
Machine learning techniques can find implied pathogenic factors in virtue of analysing and using diabetic data, with a high stability and accuracy in diabetic diagnosis. ...
doi:10.17559/tv-20190421122826
fatcat:4ez7t2d3zzawdlmpcz6ecwwwda
Body fat predicts exercise capacity in persons with Type 2 Diabetes Mellitus: A machine learning approach
2021
PLoS ONE
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Our study shows that using baseline data from a large prospective cohort, we can predict maximum exercise capacity in persons with diabetes mellitus. ...
Thereafter, we used different machine learning methods to predict maximum exercise capacity. The different machine learning models showed a strong predictive performance for both females and males. ...
In this study, we used different machine learning methods to predict the maximum exercise capacity in persons with diabetes mellitus older than 40 years of age, by analyzing the LOOK AHEA study cohort ...
doi:10.1371/journal.pone.0248039
pmid:33788855
fatcat:akf5oz5wevawtmqera5qfixxpe
Predicting Short‐term and Long‐term HbA1c Response after Insulin Initiation in Patients with Type 2 Diabetes Mellitus using Machine Learning
2019
Diabetes, obesity and metabolism
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in patients with type 2 diabetes mellitus (T2DM). ...
Machine-learning algorithm performed well in the prediction of an individual's short-term and long-term HbA1c response using baseline clinical variables. ...
In the future, we plan to develop a single multi-label machine-learning model using a large dataset from several sources. ...
doi:10.1111/dom.13860
pmid:31453664
pmcid:PMC6899933
fatcat:4qsbtivxhzhvbcxneafxd6kqyi
Machine Learning and Deep Learning Methods for Building Intelligent Systems in Medicine and Drug Discovery: A Comprehensive Survey
[article]
2021
arXiv
pre-print
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With the advancements in computer technology, there is a rapid development of intelligent systems to understand the complex relationships in data to make predictions and classifications. ...
These intelligent systems are built with machine learning and deep learning based robust models for early diagnosis of diseases and demonstrates a promising supplementary diagnostic method for frontline ...
Machine learning Machine Learning techniques are statistical models that are used to make predictions and classifications on the given data. ...
arXiv:2107.14037v1
fatcat:2xb4vsemofci7c45ethux6y6aa
A detailed survey on Prognostication of diabetes diagnosis on the basis of machine learning techniques and the detection approaches to diabetic retinopathy using Artificial Intelligence
2021
International Journal of Advanced Trends in Computer Science and Engineering
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Here plenty of classifiers in machine learning can be used, such as KNN, Random Tree, etc.They can save time and get more precise outcome when using these techniques to predict diabetes. ...
The aim of the study is to compare, assess the optimum tools as well as the techniques and advanced features focused on prediction of diabetes diagnosis based on machine learning tactics and diabetic retinopathy ...
In [6] A Comparative Study of Machine Learning Methods to Predict Diabetic Mellitus was discussed. ...
doi:10.30534/ijatcse/2021/571022021
fatcat:yn5ypebeabep7lfyhh7f23qq7y
An Ontology Driven System to Predict Diabetes with Machine Learning Techniques
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Therefore two machine learning classification algorithms namely Fine Decision Tree and Support Vector Machine are used in this experiment to detect diabetes at an early stage. ...
Therefore, two machine learning classification algorithms namely Fine Decision Tree and Support Vector Machine are used in this experiment to detect diabetes at an early stage. ...
R, D Ramesh, B R Prakash An Ontology Driven System to Predict Diabetes With Machine Learning Techniques identify and explore diabetes is a subject matter worth study. ...
doi:10.35940/ijitee.b7586.129219
fatcat:c43b6d42zvh5rcylri4fleyr74
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