Detection and Prediction of Diabetes Using Data Mining: A Comprehensive Review

Diabetes is currently one of the most common, dangerous, and costly diseases globally caused by increased blood sugar or a decrease in insulin in the body. Diabetes can have detrimental effects on people’s health if diagnosed late. Today, diabetes has become one of the challenges for health and government officials. Prevention is a priority, and taking care of people’s health without compromising their comfort is an essential need. In this study, the ensemble training methodology based on genetic algorithms was used to diagnose and predict the outcomes of diabetes mellitus accurately. This study uses the experimental data, actual data on Indian diabetics on the University of California website. Current developments in ICT, such as the Internet of Things, machine learning, and data mining, allow us to provide health strategies with more intelligent capabilities to accurately predict the outcomes of the disease in daily life and the hospital and prevent the progression of this disease and its many complications. The results show the high performance of the proposed method in diagnosing the disease, which has reached 98.8%, and 99% accuracy in this study.

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“…GA is used to reduce insignificant features in this study. We defined chromosomes as a mask for characteristics to achieve this goal [ 75 ].…”

Section: Methodsmentioning

confidence: 99%

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Abdollahi

Nouri-Moghaddam

2022

Iran J Comput Sci

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“…Diabetes, a global problem, has become one of the three biggest threats to human health. Patients with diabetes who do not receive adequate treatment will develop cardiopulmonary diseases, liver complications, nerve damage, etc., which can seriously affect their health ( 9 ). In this situation, early diagnosis and prevention of diabetes are critical.…”

Section: Related Work In the Field Of Big Datamentioning

confidence: 99%

Disease-specific data processing: An intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology

Kong

Peng

Dai

et al. 2022

Front. Public Health

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BackgroundArtificial intelligence technology has become a mainstream trend in the development of medical informatization. Because of the complex structure and a large amount of medical data generated in the current medical informatization process, big data technology to assist doctors in scientific research and analysis and obtain high-value information has become indispensable for medical and scientific research.MethodsThis study aims to discuss the architecture of diabetes intelligent digital platform by analyzing existing data mining methods and platform building experience in the medical field, using a large data platform building technology utilizing the Hadoop system, model prediction, and data processing analysis methods based on the principles of statistics and machine learning. We propose three major building mechanisms, namely the medical data integration and governance mechanism (DCM), data sharing and privacy protection mechanism (DPM), and medical application and medical research mechanism (MCM), to break down the barriers between traditional medical research and digital medical research. Additionally, we built an efficient and convenient intelligent diabetes model prediction and data analysis platform for clinical research.ResultsResearch results from this platform are currently applied to medical research at Shanghai T Hospital. In terms of performance, the platform runs smoothly and is capable of handling massive amounts of medical data in real-time. In terms of functions, data acquisition, cleaning, and mining are all integrated into the system. Through a simple and intuitive interface operation, medical and scientific research data can be processed and analyzed conveniently and quickly.ConclusionsThe platform can serve as an auxiliary tool for medical personnel and promote the development of medical informatization and scientific research. Also, the platform may provide the opportunity to deliver evidence-based digital therapeutics and support digital healthcare services for future medicine.

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“…The predictive values of this study lie on the unit interval i.e., in between 0 and 1. Khan et al [2] presented the various data mining techniques for diabetes detection, classification and prediction. In this study it is concluded that for accurate results the data should be pre-processed and parallel models should be used instead of one.…”

Section: Related Workmentioning

confidence: 99%

Data Mining Prediction Techniques in Health Care Sector

sharma

2022

J. Phys.: Conf. Ser.

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Knowledge discovery in databases (KDD) is another name of Data mining. It is an interdisciplinary area which focuses on extraction of useful knowledge from data in every sector like health, education, business etc. Nowadays, as covid pandemic is affecting everyone and due to surge in coronavirus cases causing shortage of hospital beds, oxygen supplies, vaccine and turning away patients from hospitals, put creaky health infrastructure in spotlight. The plenty of data is available in the medical field of these conditions. Data mining approaches can be used to extract useful patterns from these types of data to follow the upcoming trends. This paper focuses on various data mining techniques which can be used in medical industries for the best outcome.

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Detection and Prediction of Diabetes Using Data Mining: A Comprehensive Review

Cited by 63 publications

References 111 publications

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Disease-specific data processing: An intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology

Data Mining Prediction Techniques in Health Care Sector

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