[HTML][HTML] Comparative anatomization of data mining and fuzzy logic techniques used in diabetes prognosis

H Thakkar, V Shah, H Yagnik, M Shah - Clinical eHealth, 2021 - Elsevier
Diabetes is an ailment in which glucose level increase in at high rates in blood due to body's
inability to metabolize it. This happens when body does not produce sufficient amount of …

Detection and prediction of diabetes using data mining: a comprehensive review

FA Khan, K Zeb, M Al-Rakhami, A Derhab… - IEEE …, 2021 - ieeexplore.ieee.org
Diabetes is one of the most rapidly growing chronic diseases, which has affected millions of
people around the globe. Its diagnosis, prediction, proper cure, and management are …

Performance analysis of machine learning algorithms on diabetes dataset using big data analytics

PS Kumar, S Pranavi - 2017 international conference on …, 2017 - ieeexplore.ieee.org
New Technologies such as Big Data and Cloud is playing a vital role in providing solutions
to Healthcare problems. Now-a-days healthcare data is growing very drastically day-by-day …

[PDF][PDF] Prognosis of diabetes using data mining approach-fuzzy C means clustering and support vector machine

R Sanakal, T Jayakumari - International Journal of Computer Trends and …, 2014 - Citeseer
Clinical decision-making needs available information to be the guidance for physicians.
Nowadays, data mining method is applied in medical research in order to analyze large …

Machine and deep learning techniques for the prediction of diabetics: a review

SKS Modak, VK Jha - Multimedia Tools and Applications, 2024 - Springer
Diabetes has become one of the significant reasons for public sickness and death in
worldwide. By 2019, diabetes had affected more than 463 million people worldwide …

[PDF][PDF] Comparative analysis of predicting diabetes using machine learning techniques

KM Varma, BS Panda - J. Emerg. Technol. Innov. Res, 2019 - researchgate.net
Diabetes is a chronic disease caused due to the expanded level of sugar addiction in the
blood. Various automated information systems were outlined utilizing various classifiers for …

A euclidean group assessment on semi-supervised clustering for healthcare clinical implications based on real-life data

MN Sohail, J Ren, M Uba Muhammad - International journal of …, 2019 - mdpi.com
The grouping of clusters is an important task to perform for the initial stage of clinical
implication and diagnosis of a disease. The researchers performed evaluation work on …

Predicting diabetes mellitus using data mining techniques

J Steffi, R Balasubramanian, MKA Kumar - International Journal of …, 2018 - rjwave.org
Diabetes is a chronic disease caused due to the expanded level of sugar addiction in the
blood. Various automated information systems were outlined utilizing various classifiers for …

Comparative Analysis on Diabetes Dataset Using Machine Learning Algorithms

BS Murthy, J Srilatha - 2021 6th International Conference on …, 2021 - ieeexplore.ieee.org
The future of healthcare will go hand in hand with technology. Machine Learning has the
ability to classify a huge amount of data and learn from them plays a vital role in predicting …

An accurate clinical implication assessment for diabetes mellitus prevalence based on a study from Nigeria

MN Sohail, R Jiadong, MU Muhammad, ST Chauhdary… - Processes, 2019 - mdpi.com
The increasing rate of diabetes is found across the planet. Therefore, the diagnosis of pre-
diabetes and diabetes is important in populations with extreme diabetes risk. In this study, a …