A Survey on Data Mining Approaches to Diabetes Disease Diagnosis and Prognosis

Kumar, B. Senthil; Gunavathi, R.

doi:10.17148/ijarcce.2016.512105

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…The burden of diabetes is increasing worldwide, including in developing countries like Ethiopia. The International Diabetes Federation Association reported Ethiopia to be ranked 3rd in Africa with 1.4 million DM and a prevalence of 3.32 by the year 2012 Kumar and D. G. R (2016). Diabetes affects all segments of the population, regardless of age and sex Eapen (2004) Diabetes of all kinds can cause complications that will increase the general risk of dying prematurely.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Diabetes Screening by Using Data Mining Algorithms

Zemedkun

2021

Int. J. Eng. Sci. Tech

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Diabetes is one of the most common non-communicable diseases in the world. Diabetes affects the ability to produce the hormone insulin. Thus, complications may occur if diabetes remains untreated and unidentified. That features a significant contribution to increased morbidity, mortality, and admission rates of patients in both developed and developing countries. When disease is not detected early, it leads to complications. Medical records of the cases were retrospective. Anthropometric and biochemical information was collected. From this data, four ML classification algorithms, including Decision Tree (J48), Naive-Bayes, PART rule induction, and JRIP, were used to prognosticate diabetes. Precision, recall, F-Measure, Receiver Operating Characteristics (ROC) scores, and the confusion matrix were calculated to determine the performance of the various algorithms. The performance was also measured by sensitivity and specificity. They have high classification accuracy and are generally comparable in predicting diabetes and free diabetes patients. Among the selected algorithms tested, the Decision Tree Classifier (J48) algorithm scored the highest accuracy and was the best predictor, with a classification accuracy of 92.74%.

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Section: Introductionmentioning

confidence: 99%

Prediction of Diabetes Screening by Using Data Mining Algorithms

Zemedkun

2021

Int. J. Eng. Sci. Tech

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Diabetes disease prediction using significant attribute selection and classification approach

Tiwari¹,

Singh²

2021

J. Phys.: Conf. Ser.

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Data Mining performs a major role in healthcare services because disease recognition and investigation contains a vast amount of data. These conditions generate several data managing problems, and to operate efficiently. The healthcare datasets are undefined and influential and it is extremely monotonous to manage and to operate. To get better of the exceeding problems, numerous analyses present various ML algorithms for different disease examination and prediction. The undertaking of disease identification and prediction is an element of classification and forecasting. In this paper, diabetes is estimated by major characteristics and the relation of contradictory characteristics is also categorized. Significant features selection was done via the recursive feature elimination with random forest. The estimation of our system specifies a powerful alliance of diabetes with (BMI) and with glucose level was drawing out using the Apriori approach. XGBoost has examined for the estimation of diabetes. The XGBoost gives better accuracy of 78.91% compared to the ANN approach and might help support medicinal professionals through treatment decisions.

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A Survey on Data Mining Approaches to Diabetes Disease Diagnosis and Prognosis

Cited by 2 publications

References 15 publications

Prediction of Diabetes Screening by Using Data Mining Algorithms

Prediction of Diabetes Screening by Using Data Mining Algorithms

Diabetes disease prediction using significant attribute selection and classification approach

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