Prediction and diagnosis of diabetes mellitus — A machine learning approach

Vijayan, V. Veena; Anjali, C

doi:10.1109/raics.2015.7488400

Cited by 130 publications

(42 citation statements)

References 8 publications

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“…Breault et al [9] shows rising growth of diabetes in US which makes it very important issue to ponder on. Different classification technique [10,11] like decision tree (DT), support vector machine (SVM), Naive Bayes (NB), decision stump (DS), when evaluated for performance check without boosting gave accuracy as 76%, 79.68%, 78.1%, 74.47% respectively and the performance evaluation of Adaboost with DT, SVM, NB, DS as base classifier resulted in the accuracy improvement except SVM which does not show any accuracy development as mentioned in the Table 1 below.…”

Section: Literature Surveymentioning

confidence: 99%

Prediction and diagnosis of future diabetes risk: a machine learning approach

et al. 2019

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Machine learning is a subset of Artificial Intelligence when combined with Data Mining techniques plays a promising role in the field of prediction. We live in an era where data generation is exponential with time but if the generated data is not put to work or not converted to knowledge data, its generation is of no use. Similarly, in Healthcare also, data availability is high, so is the need to extract the information from it for better prognosis, diagnosis, treatment, drug development, and overall healthcare. In this research, we have tried to focus more on diagnosis of Diabetes disease, which is one of the fastest growing chronic diseases all over the world as declared by World Health Organization in the year 2014. We have also tried to show the different techniques like Gradient Boosting, Logistic Regression and Naive Bayes, which can be used for the diagnosis of diabetes disease with attained accuracy as 86% for the Gradient Boosting, 79% for Logistic Regression and 77% for Naive Bayes.

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Section: Literature Surveymentioning

confidence: 99%

Prediction and diagnosis of future diabetes risk: a machine learning approach

et al. 2019

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“…A functional relationship of a dependent variable Y with the independent variables X 1 , X 2 ,…X k and which involving parameters β 0 , β 1 ,β 2, …, β k of the type. The regression mode defined as equation (2). Y =  (X 1 , X 2 ,…X k | β 0 , β 1 ,β 2, …, β k ) +  (2) Where  is the form of the equation and  indicates the random variable distributed with mean 0 and variance is called as the residual error term.…”

Section: Logistic Regression Classificationmentioning

confidence: 99%

“…Diabetes is affected not only by various factors like hereditary factor, height, weight, and insulin but the main reason is considered as glucose concentration of all factors. The early diabetic identification is only solution to stay away from the complications [2]. The researchers are to diagnosing the diseases for conducting experiments using various classification methods of Machine Learning approaches like Naive Bayes, J48, SVM, Decision Tree, etc.…”

Section: Introductionmentioning

confidence: 99%

Data Mining Technique for Diabetes Diagnosis using Classification Algorithms

Priya*,

Karthikeyan

2019

IJRTE

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 Abstract: Diabetes mellitus is defined as a one of the chronic and deadliest diseases which combined with abnormally high level of sugar (glucose) in the blood. The classification technique helps in diagnosis the symptoms at starting stages. This paper focused to prognosticate the chance of diabetes in patients with extremely correct classification of Diabetes. The classification algorithms viz., Naïve Bayes, Logistic Regression, and Decision Tree can be used to detect diabetes at an early stage. The algorithm performances are evaluated based on various measures like Recall, Precision, and F-Measure. Experiments are conducted where the time complexity of each of the algorithm is measured. Accuracy is also measured over correct classification and misclassification instances, observed that a Logistic Regression algorithm has much better performance when compared to the other type classifications. Using Receiver OperatingCharacteristic curves the results are verified in a systematic manner.

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“…So the changes with the boosting and arcing algorithms have the ability to reduce bias as well as variance. Using a random selection of features to segment, each node yields error rates that compare favorably to Adaboost [18] but it more robust with respect to noise. In the diabetes diagnosis process, Machine learning approaches are used.…”

Section: Data Mining Information Systemmentioning

confidence: 99%

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

Kumar¹,

Gunavathi²

2016

International Journal of Advanced Research in Computer and Comm

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Data Mining plays an important role in the field of healthcare, because disease diagnosis and analysis have huge size of data. These circumstances create huge number of data handling issues, and that to be handled effectively. The health dataset's are uncertain and dynamic in nature and it is very tedious to maintain and to manipulate. To overcome the above issues, several studies introduced numerous Machine learning approaches for various disease diagnosis and prognosis. This paper a different data mining and machine learning techniques used in diabetes are analyzed and compared. The task of disease diagnosis and prognosis is a part of classification and prediction. The recent and popular data mining techniques used in clinical data includes Bayesian, Random forest algorithms, Artificial Neural network, SVM and Decision Tree etc. This paper gives the problems and findings about those techniques with various factors.

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Prediction and diagnosis of diabetes mellitus — A machine learning approach

Cited by 130 publications

References 8 publications

Prediction and diagnosis of future diabetes risk: a machine learning approach

Prediction and diagnosis of future diabetes risk: a machine learning approach

Data Mining Technique for Diabetes Diagnosis using Classification Algorithms

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

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