Soft computing techniques for type-2 diabetes data classification
Soft computing techniques for type-2 diabetes data classification:

Diabetes Mellitus (DM, commonly referred to as diabetes, is a metabolic disorder in which there are high blood sugar levels over a prolonged period. Lack of sufficient insulin causes presence of excess sugar levels in the blood. As a result the glucose levels in diabetic patients are more than norma...

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Bibliographische Detailangaben
Hauptverfasser: Cheruku, Ramalingaswamy (VerfasserIn), Edla, Damodar Reddy (VerfasserIn), Kuppili, Venkatanareshbabu (VerfasserIn)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: Boca Raton CRC Press, Taylor & Francis Group 2020
Schlagworte:
COMPUTERS / Computer Science / bisacsh
COMPUTERS / Bioinformatics / bisacsh
COMPUTERS / Information Technology / bisacsh
Medical informatics
Diabetes > Data processing
Online-Zugang:URL des Erstveröffentlichers
Zusammenfassung:Diabetes Mellitus (DM, commonly referred to as diabetes, is a metabolic disorder in which there are high blood sugar levels over a prolonged period. Lack of sufficient insulin causes presence of excess sugar levels in the blood. As a result the glucose levels in diabetic patients are more than normal ones. It has symptoms like frequent urination, increased hunger, increase thirst and high blood sugar. There are mainly three types of diabetes namely type-1, type-2 and gestational diabetes. Type-1 DM occurs due to immune system mistakenly attacks and destroys the beta-cells and Type-2 DM occurs due to insulin resistance. Gestational DM occurs in women during pregnancy due to insulin blocking by pregnancy harmones. Among these three types of DM, type-2 DM is more prevalence, and impacting so many millions of people across the world. Classification and predictive systems are actually reliable in the health care sector to explore hidden patterns in the patients data. These systems aid, medical professionals to enhance their diagnosis, prognosis along with remedy organizing techniques. The less percentage of improvement in classifier predictive accuracy is very important for medical diagnosis purposes where mistakes can cause a lot of damage to patient's life. Hence, we need a more accurate classification system for prediction of type-2 DM. Although, most of the above classification algorithms are efficient, they failed to provide good accuracy with low computational cost. In this book, we proposed various classification algorithms using soft computing techniques like Neural Networks (NNs), Fuzzy Systems (FS) and Swarm Intelligence (SI). The experimental results demonstrate that these algorithms are able to produce high classification accuracy at less computational cost. The contributions presented in this book shall attempt to address the following objectives using soft computing approaches for identification of diabetes mellitus. Introuducing an optimized RBFN m
Beschreibung:"A Chapman & Hall book."
PrefaceAuthor BioIntroductionLiterature SurveyClassifcation of Type-2 Diabetes using CVI based RBFNClassifcation of Type-2 Diabetes using Spider Monkey Crisp Rule MinerClassifcation of Type-2 Diabetes using Bat based Fuzzy Rule MinerClassifcation of Type-2 Diabetes using Dual-Stage Cascade NetworkClassifcation of Type-2 Diabetes using Bi-Level Ensemble NetworkIntelli-DRM: An Intelligent Computational Model for Fore-casting Severity of Diabetes MellitusConclusion and Future ResearchBibliography
Beschreibung:1 online resource (xvi, 152 pages)
ISBN:9780429281051
0429281056
9781000048148
1000048144
9781000048186
1000048187
DOI:10.4324/9780429281051

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