Hybrid prediction model for Type-2 diabetic patients

Patil, B. M.; Joshi, Rohit; Toshniwal, Durga

doi:10.1016/j.eswa.2010.05.078

Cited by 157 publications

(75 citation statements)

References 21 publications

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“…While the accuracy of k-NN on diabetes detection problem ranges between 71-78% [16,17], a more sensitive performance with accuracy of 92.38% was achieved with a hybrid model of k-NN and C4.5 algorithms [18,19].…”

Section: Clustering Techniquesmentioning

confidence: 99%

Computational Intelligence in Early Diabetes Diagnosis: A Review

Shankaracharya¹,

Odedra²,

Samanta³

et al. 2010

Rev Diabet Stud

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The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research.

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Section: Clustering Techniquesmentioning

confidence: 99%

Computational Intelligence in Early Diabetes Diagnosis: A Review

Shankaracharya¹,

Odedra²,

Samanta³

et al. 2010

Rev Diabet Stud

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show abstract

“…A tool used for this purpose is WEKA and the data set was PIMA Indian diabetes data set. This hybrid model has achieved 92.38% accuracy [26]. The details of the hybrid model are shown in Fig.…”

Section: Different Prediction Models Used For Diabetesmentioning

confidence: 99%

Survey on clinical prediction models for diabetes prediction

2017

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“…In most of the data mining studies that were investigated, more attention has been paid to several medical fields, including RA [8,9,10], cardiovascular diseases [11,12,13,14,15,16], cancer [17,18,19,20], lung [21,22,23], traumatic brain injury [24,25,26] and diabetes [27,28,29].…”

Section: Related Workmentioning

confidence: 99%