Prognosis of Diabetes Using Data mining Approach-Fuzzy C Means Clustering and Support Vector Machine

Sanakal, Ravi; Jayakumari, S.

doi:10.14445/22312803/ijctt-v11p120

Cited by 53 publications

(17 citation statements)

References 6 publications

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“…As per the survey of above papers we can find many gaps that are to be filled, which are usage of larger dataset [23,34], outlier detection [35], improving prediction model [34], integration of optimization techniques to hybrid prediction model [33], implementation of prediction models for other diseases on android mobile [31], development of prediction model that include type 1 treatment plans with more attributes [30], usage of datasets of multiple classes [4].…”

Section: Discussionmentioning

confidence: 99%

Survey on clinical prediction models for diabetes prediction

2017

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Section: Discussionmentioning

confidence: 99%

Survey on clinical prediction models for diabetes prediction

2017

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“…The proposed method predicts whether a patient possesses diabetic or not. The accuracy level of FCM is 94.3% and positive predicted value is 88.57% [27].…”

Section: International Journal Of Computer Applications (0975 -8887)mentioning

confidence: 93%

Preserving Sensitive Information using Fuzzy C-Means Approach

Kiran¹,

Puri²,

Srinivasa³

2018

IJCA

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Privacy is one of the important issues now days as privacy is linked with multidimensional issues; security, sentiment, fear, emotions, threats etc. Protecting privacy is as much as data utilization. In this day and age, data is getting generated largely by various industries. Medical industry is one of them. Providing safe access controls and privacy preservation are the primary concerns in the development of medical applications. Medical data possess sensitive information. According to the author, privacy should be preserved at all levels; storage level, to view level to knowledge discovery level. At view level, very limited approaches are proposed to protect the privacy of the medical data. This paper implements Fuzzy C means approach to protect the sensitive data while viewing blood donor data online. In this paper, a sample blood donor records are extracted to categorize the data into high sensitive data and low sensitive data using fuzzy C means rules. Subsequently, the model teaches the underlying relations to perform categorization based on the input. This paper describes the experiment in view of privacy preserving data mining. The experiment is simulated using MATLAB and shows satisfactory result.

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“…Diagnosis of DM has been extensively studied under many data mining techniques [21][22][23]. The most suitable data mining subfield for disease diagnosis is classification [2].…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction

et al. 2019

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Early diagnosis of diabetes mellitus (DM) is critical to prevent its serious complications. An ensemble of classifiers is an effective way to enhance classification performance, which can be used to diagnose complex diseases, such as DM. This paper proposes an ensemble framework to diagnose DM by optimally employing multiple classifiers based on bagging and random subspace techniques. The proposed framework combines seven of the most suitable and heterogeneous data mining techniques, each with a separate set of suitable features. These techniques are k-nearest neighbors, naïve Bayes, decision tree, support vector machine, fuzzy decision tree, artificial neural network, and logistic regression. The framework is designed accurately by selecting, for every sub-dataset, the most suitable feature set and the most accurate classifier. It was evaluated using a real dataset collected from electronic health records of Mansura University Hospitals (Mansura, Egypt). The resulting framework achieved 90% of accuracy, 90.2% of recall = 90.2%, and 94.9% of precision. We evaluated and compared the proposed framework with many other classification algorithms. An analysis of the results indicated that the proposed ensemble framework significantly outperforms all other classifiers. It is a successful step towards constructing a personalized decision support system, which could help physicians in daily clinical practice.

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Prognosis of Diabetes Using Data mining Approach-Fuzzy C Means Clustering and Support Vector Machine

Cited by 53 publications

References 6 publications

Survey on clinical prediction models for diabetes prediction

Survey on clinical prediction models for diabetes prediction

Preserving Sensitive Information using Fuzzy C-Means Approach

A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction

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